Method and system for collective autonomous operation database for autonomous vehicles

ABSTRACT

Systems of an electrical vehicle and the operations thereof are provided that forms ad hoc autonomous vehicle networks to conserve bandwidth in receiving information from a remote source and sending information to the remote source.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 15/393,139, filed Dec. 28, 2016, entitled “Methodand System for Collective Autonomous Operation Database for AutonomousVehicles”, which claims the benefits of and priority, under 35 U.S.C. §119(e), to U.S. Provisional Application Ser. No. 62/418,620, filed Nov.7, 2016, entitled “Self-Driving Control Systems for the Next GenerationVehicle,” the entire disclosures of which are both hereby incorporatedby reference, in their entirety, for all that they teach and for allpurposes.

FIELD

The present disclosure is generally directed to vehicle systems, inparticular, toward electric and/or hybrid-electric vehicles.

BACKGROUND

In recent years, transportation methods have changed substantially. Thischange is due in part to a concern over the limited availability ofnatural resources, a proliferation in personal technology, and asocietal shift to adopt more exterior environmentally friendlytransportation solutions. These considerations have encouraged thedevelopment of a number of new flexible-fuel vehicles, hybrid-electricvehicles, and electric vehicles.

While these vehicles appear to be new they are generally implemented asa number of traditional subsystems that are merely tied to analternative power source. In fact, the design and construction of thevehicles is limited to standard frame sizes, shapes, materials, andtransportation concepts. Among other things, these limitations fail totake advantage of the benefits of new technology, power sources, andsupport infrastructure. In particular, the implementation of anartificially intelligent vehicle has lagged far behind the developmentvehicle subsystems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a vehicle in accordance with embodiments of the presentdisclosure;

FIG. 2 shows a plan view of the vehicle in accordance with at least someembodiments of the present disclosure;

FIG. 3 is a block diagram of an embodiment of a communicationenvironment of the vehicle in accordance with embodiments of the presentdisclosure;

FIG. 4 shows an embodiment of the instrument panel of the vehicleaccording to one embodiment of the present disclosure;

FIG. 5 is a block diagram of an embodiment of a communications subsystemof the vehicle;

FIG. 6 is a block diagram of a computing environment associated with theembodiments presented herein;

FIG. 7 is a block diagram of a computing device associated with one ormore components described herein;

FIG. 8 is block diagram of a computational system in a vehicle andassociated with one or more components described herein;

FIG. 9 is a block diagram of an autonomous driving vehicle systemaccording to an embodiment;

FIG. 10 is a flow chart associated with one or more embodimentspresented herein;

FIG. 11 is a flow chart associated with one or more embodimentspresented herein;

FIG. 12 is a flow chart associated with one or more embodimentspresented herein;

FIG. 13 is a flow chart associated with one or more embodimentspresented herein; and

FIG. 14 is a block diagram of a computing system associated with one ormore components described herein.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in connectionwith a vehicle, and in some embodiments, an electric vehicle,rechargeable electric vehicle, and/or hybrid-electric vehicle andassociated systems.

Embodiments can provide an intelligent autonomous vehicle control systemthat augments the learned behaviors from autonomous vehicle drivingoperations with temporally, spatially, or event-limited behaviorsreceived from the intelligent autonomous vehicle control system. Thetemporally, spatially, or event-limited behaviors can be learned by thecontrol system monitoring the behaviors of multiple autonomous vehicles.

As will be appreciated, a “behavior” is the collection of a selected setof the executions of the instructions of a software program. It can bedefined in many ways, including event-based and/or input-output basedobservations.

FIG. 1 shows a perspective view of a vehicle 100 in accordance withembodiments of the present disclosure. The electric vehicle 100comprises a vehicle front 110, vehicle aft or rear 120, vehicle roof130, at least one vehicle side 160, a vehicle undercarriage 140, and avehicle interior 150. In any event, the vehicle 100 may include a frame104 and one or more body panels 108 mounted or affixed thereto. Thevehicle 100 may include one or more interior components (e.g.,components inside an interior space 150, or user space, of a vehicle100, etc.), exterior components (e.g., components outside of theinterior space 150, or user space, of a vehicle 100, etc.), drivesystems, controls systems, structural components, etc.

Although shown in the form of a car, it should be appreciated that thevehicle 100 described herein may include any conveyance or model of aconveyance, where the conveyance was designed for the purpose of movingone or more tangible objects, such as people, animals, cargo, and thelike. The term “vehicle” does not require that a conveyance moves or iscapable of movement. Typical vehicles may include but are in no waylimited to cars, trucks, motorcycles, busses, automobiles, trains,railed conveyances, boats, ships, marine conveyances, submarineconveyances, airplanes, space craft, flying machines, human-poweredconveyances, and the like.

In some embodiments, the vehicle 100 may include a number of sensors,devices, and/or systems that are capable of assisting in drivingoperations. Examples of the various sensors and systems may include, butare in no way limited to, one or more of cameras (e.g., independent,stereo, combined image, etc.), infrared (IR) sensors, radio frequency(RF) sensors, ultrasonic sensors (e.g., transducers, transceivers,etc.), RADAR sensors (e.g., object-detection sensors and/or systems),LIDAR systems, odometry sensors and/or devices (e.g., encoders, etc.),orientation sensors (e.g., accelerometers, gyroscopes, magnetometer,etc.), navigation sensors and systems (e.g., GPS, etc.), and otherranging, imaging, and/or object-detecting sensors. The sensors may bedisposed in an interior space 150 of the vehicle 100 and/or on anoutside of the vehicle 100. In some embodiments, the sensors and systemsmay be disposed in one or more portions of a vehicle 100 (e.g., theframe 104, a body panel, a compartment, etc.).

The vehicle sensors and systems may be selected and/or configured tosuit a level of operation associated with the vehicle 100. Among otherthings, the number of sensors used in a system may be altered toincrease or decrease information available to a vehicle control system(e.g., affecting control capabilities of the vehicle 100). Additionallyor alternatively, the sensors and systems may be part of one or moreadvanced driver assistance systems (ADAS) associated with a vehicle 100.In any event, the sensors and systems may be used to provide drivingassistance at any level of operation (e.g., from fully-manual tofully-autonomous operations, etc.) as described herein.

The various levels of vehicle control and/or operation can be describedas corresponding to a level of autonomy associated with a vehicle 100for vehicle driving operations. For instance, at Level 0, orfully-manual driving operations, a driver (e.g., a human driver) may beresponsible for all the driving control operations (e.g., steering,accelerating, braking, etc.) associated with the vehicle. Level 0 may bereferred to as a “No Automation” level. At Level 1, the vehicle may beresponsible for a limited number of the driving operations associatedwith the vehicle, while the driver is still responsible for most drivingcontrol operations. An example of a Level 1 vehicle may include avehicle in which the throttle control and/or braking operations may becontrolled by the vehicle (e.g., cruise control operations, etc.). Level1 may be referred to as a “Driver Assistance” level. At Level 2, thevehicle may collect information (e.g., via one or more drivingassistance systems, sensors, etc.) about an environment of the vehicle(e.g., surrounding area, roadway, traffic, ambient conditions, etc.) anduse the collected information to control driving operations (e.g.,steering, accelerating, braking, etc.) associated with the vehicle. In aLevel 2 autonomous vehicle, the driver may be required to perform otheraspects of driving operations not controlled by the vehicle. Level 2 maybe referred to as a “Partial Automation” level. It should be appreciatedthat Levels 0-2 all involve the driver monitoring the driving operationsof the vehicle.

At Level 3, the driver may be separated from controlling all the drivingoperations of the vehicle except when the vehicle makes a request forthe operator to act or intervene in controlling one or more drivingoperations. In other words, the driver may be separated from controllingthe vehicle unless the driver is required to take over for the vehicle.Level 3 may be referred to as a “Conditional Automation” level. At Level4, the driver may be separated from controlling all the drivingoperations of the vehicle and the vehicle may control driving operationseven when a user fails to respond to a request to intervene. Level 4 maybe referred to as a “High Automation” level. At Level 5, the vehicle cancontrol all the driving operations associated with the vehicle in alldriving modes. The vehicle in Level 5 may continually monitor traffic,vehicular, roadway, and/or exterior environmental conditions whiledriving the vehicle. In Level 5, there is no human driver interactionrequired in any driving mode. Accordingly, Level 5 may be referred to asa “Full Automation” level. It should be appreciated that in Levels 3-5the vehicle, and/or one or more automated driving systems associatedwith the vehicle, monitors the driving operations of the vehicle and thedriving environment.

As shown in FIG. 1, the vehicle 100 may, for example, include at leastone of a ranging and imaging system 112 (e.g., LIDAR, etc.), an imagingsensor 116A, 116F (e.g., camera, IR, etc.), a radio object-detection andranging system sensors 116B (e.g., RADAR, RF, etc.), ultrasonic sensors116C, and/or other object-detection sensors 116D, 116E. In someembodiments, the LIDAR system 112 and/or sensors may be mounted on aroof 130 of the vehicle 100. In one embodiment, the RADAR sensors 116Bmay be disposed at least at a front 110, aft 120, or side 160 of thevehicle 100. Among other things, the RADAR sensors may be used tomonitor and/or detect a position of other vehicles, pedestrians, and/orother objects near, or proximal to, the vehicle 100. While shownassociated with one or more areas of a vehicle 100, it should beappreciated that any of the sensors and systems 116A-K, 112 illustratedin FIGS. 1 and 2 may be disposed in, on, and/or about the vehicle 100 inany position, area, and/or zone of the vehicle 100.

Referring now to FIG. 2, a plan view of a vehicle 100 will be describedin accordance with embodiments of the present disclosure. In particular,FIG. 2 shows a vehicle sensing environment 200 at least partiallydefined by the sensors and systems 116A-K, 112 disposed in, on, and/orabout the vehicle 100. Each sensor 116A-K may include an operationaldetection range R and operational detection angle a. The operationaldetection range R may define the effective detection limit, or distance,of the sensor 116A-K. In some cases, this effective detection limit maybe defined as a distance from a portion of the sensor 116A-K (e.g., alens, sensing surface, etc.) to a point in space offset from the sensor116A-K. The effective detection limit may define a distance, beyondwhich, the sensing capabilities of the sensor 116A-K deteriorate, failto work, or are unreliable. In some embodiments, the effective detectionlimit may define a distance, within which, the sensing capabilities ofthe sensor 116A-K are able to provide accurate and/or reliable detectioninformation. The operational detection angle a may define at least oneangle of a span, or between horizontal and/or vertical limits, of asensor 116A-K. As can be appreciated, the operational detection limitand the operational detection angle a of a sensor 116A-K together maydefine the effective detection zone 216A-D (e.g., the effectivedetection area, and/or volume, etc.) of a sensor 116A-K.

In some embodiments, the vehicle 100 may include a ranging and imagingsystem 112 such as LIDAR, or the like. The ranging and imaging system112 may be configured to detect visual information in an environmentsurrounding the vehicle 100. The visual information detected in theenvironment surrounding the ranging and imaging system 112 may beprocessed (e.g., via one or more sensor and/or system processors, etc.)to generate a complete 360-degree view of an environment 200 around thevehicle. The ranging and imaging system 112 may be configured togenerate changing 360-degree views of the environment 200 in real-time,for instance, as the vehicle 100 drives. In some cases, the ranging andimaging system 112 may have an effective detection limit 204 that issome distance from the center of the vehicle 100 outward over 360degrees. The effective detection limit 204 of the ranging and imagingsystem 112 defines a view zone 208 (e.g., an area and/or volume, etc.)surrounding the vehicle 100. Any object falling outside of the view zone208 is in the undetected zone 212 and would not be detected by theranging and imaging system 112 of the vehicle 100.

Sensor data and information may be collected by one or more sensors orsystems 116A-K, 112 of the vehicle 100 monitoring the vehicle sensingenvironment 200. This information may be processed (e.g., via aprocessor, computer-vision system, etc.) to determine targets (e.g.,objects, signs, people, markings, roadways, conditions, etc.) inside oneor more detection zones 208, 216A-D associated with the vehicle sensingenvironment 200. In some cases, information from multiple sensors 116A-Kmay be processed to form composite sensor detection information. Forexample, a first sensor 116A and a second sensor 116F may correspond toa first camera 116A and a second camera 116F aimed in a forwardtraveling direction of the vehicle 100. In this example, imagescollected by the cameras 116A, 116F may be combined to form stereo imageinformation. This composite information may increase the capabilities ofa single sensor in the one or more sensors 116A-K by, for example,adding the ability to determine depth associated with targets in the oneor more detection zones 208, 216A-D. Similar image data may be collectedby rear view cameras (e.g., sensors 116G, 116H) aimed in a rearwardtraveling direction vehicle 100.

In some embodiments, multiple sensors 116A-K may be effectively joinedto increase a sensing zone and provide increased sensing coverage. Forinstance, multiple RADAR sensors 116B disposed on the front 110 of thevehicle may be joined to provide a zone 216B of coverage that spansacross an entirety of the front 110 of the vehicle. In some cases, themultiple RADAR sensors 116B may cover a detection zone 216B thatincludes one or more other sensor detection zones 216A. Theseoverlapping detection zones may provide redundant sensing, enhancedsensing, and/or provide greater detail in sensing within a particularportion (e.g., zone 216A) of a larger zone (e.g., zone 216B).Additionally or alternatively, the sensors 116A-K of the vehicle 100 maybe arranged to create a complete coverage, via one or more sensing zones208, 216A-D around the vehicle 100. In some areas, the sensing zones216C of two or more sensors 116D, 116E may intersect at an overlap zone220. In some areas, the angle and/or detection limit of two or moresensing zones 216C, 216D (e.g., of two or more sensors 116E, 116J, 116K)may meet at a virtual intersection point 224.

The vehicle 100 may include a number of sensors 116E, 116G, 116H, 116J,116K disposed proximal to the rear 120 of the vehicle 100. These sensorscan include, but are in no way limited to, an imaging sensor, camera,IR, a radio object-detection and ranging sensors, RADAR, RF, ultrasonicsensors, and/or other object-detection sensors. Among other things,these sensors 116E, 116G, 116H, 116J, 116K may detect targets near orapproaching the rear of the vehicle 100. For example, another vehicleapproaching the rear 120 of the vehicle 100 may be detected by one ormore of the ranging and imaging system (e.g., LIDAR) 112, rear-viewcameras 116G, 116H, and/or rear facing RADAR sensors 116J, 116K. Asdescribed above, the images from the rear-view cameras 116G, 116H may beprocessed to generate a stereo view (e.g., providing depth associatedwith an object or environment, etc.) for targets visible to both cameras116G, 116H. As another example, the vehicle 100 may be driving and oneor more of the ranging and imaging system 112, front-facing cameras116A, 116F, front-facing RADAR sensors 116B, and/or ultrasonic sensors116C may detect targets in front of the vehicle 100. This approach mayprovide critical sensor information to a vehicle control system in atleast one of the autonomous driving levels described above. Forinstance, when the vehicle 100 is driving autonomously (e.g., Level 3,Level 4, or Level 5) and detects other vehicles stopped in a travelpath, the sensor detection information may be sent to the vehiclecontrol system of the vehicle 100 to control a driving operation (e.g.,braking, decelerating, etc.) associated with the vehicle 100 (in thisexample, slowing the vehicle 100 as to avoid colliding with the stoppedother vehicles). As yet another example, the vehicle 100 may beoperating and one or more of the ranging and imaging system 112, and/orthe side-facing sensors 116D, 116E (e.g., RADAR, ultrasonic, camera,combinations thereof, and/or other type of sensor), may detect targetsat a side of the vehicle 100. It should be appreciated that the sensors116A-K may detect a target that is both at a side 160 and a front 110 ofthe vehicle 100 (e.g., disposed at a diagonal angle to a centerline ofthe vehicle 100 running from the front 110 of the vehicle 100 to therear 120 of the vehicle). Additionally or alternatively, the sensors116A-K may detect a target that is both, or simultaneously, at a side160 and a rear 120 of the vehicle 100 (e.g., disposed at a diagonalangle to the centerline of the vehicle 100).

FIG. 3 is a is a block diagram of an embodiment of a communicationenvironment 300 of the vehicle 100 in accordance with embodiments of thepresent disclosure. The communication system 300 may include one or morevehicle driving vehicle sensors and systems 304, sensor processors 340,sensor data memory 344, vehicle control system 348, communicationssubsystem 350, control data 364, computing devices 368, display devices372, and other components 374 that may be associated with a vehicle 100.These associated components may be electrically and/or communicativelycoupled to one another via at least one bus 360. In some embodiments,the one or more associated components may send and/or receive signalsacross a communication network 352 to at least one of a navigationsource 356A, a control source 356B, or some other entity 356N.

In accordance with at least some embodiments of the present disclosure,the communication network 352 may comprise any type of knowncommunication medium or collection of communication media and may useany type of protocols, such as SIP, TCP/IP, SNA, IPX, AppleTalk, and thelike, to transport messages between endpoints. The communication network352 may include wired and/or wireless communication technologies. TheInternet is an example of the communication network 352 that constitutesan Internet Protocol (IP) network consisting of many computers,computing networks, and other communication devices located all over theworld, which are connected through many telephone systems and othermeans. Other examples of the communication network 104 include, withoutlimitation, a standard Plain Old Telephone System (POTS), an IntegratedServices Digital Network (ISDN), the Public Switched Telephone Network(PSTN), a Local Area Network (LAN), such as an Ethernet network, aToken-Ring network and/or the like, a Wide Area Network (WAN), a virtualnetwork, including without limitation a virtual private network (“VPN”);the Internet, an intranet, an extranet, a cellular network, an infra-rednetwork; a wireless network (e.g., a network operating under any of theIEEE 802.9 suite of protocols, the Bluetooth® protocol known in the art,and/or any other wireless protocol), and any other type ofpacket-switched or circuit-switched network known in the art and/or anycombination of these and/or other networks. In addition, it can beappreciated that the communication network 352 need not be limited toany one network type, and instead may be comprised of a number ofdifferent networks and/or network types. The communication network 352may comprise a number of different communication media such as coaxialcable, copper cable/wire, fiber-optic cable, antennas fortransmitting/receiving wireless messages, and combinations thereof

The driving vehicle sensors and systems 304 may include at least onenavigation 308 (e.g., global positioning system (GPS), etc.),orientation 312, odometry 316, LIDAR 320, RADAR 324, ultrasonic 328,camera 332, infrared (IR) 336, and/or other sensor or system 338. Thesedriving vehicle sensors and systems 304 may be similar, if notidentical, to the sensors and systems 116A-K, 112 described inconjunction with FIGS. 1 and 2.

The navigation sensor 308 may include one or more sensors havingreceivers and antennas that are configured to utilize a satellite-basednavigation system including a network of navigation satellites capableof providing geolocation and time information to at least one componentof the vehicle 100. Examples of the navigation sensor 308 as describedherein may include, but are not limited to, at least one of Garmin® GLO™family of GPS and GLONASS combination sensors, Garmin® GPS 15x™ familyof sensors, Garmin® GPS 16x™ family of sensors with high-sensitivityreceiver and antenna, Garmin® GPS 18x OEM family of high-sensitivity GPSsensors, Dewetron DEWE-VGPS series of GPS sensors, GlobalSat 1-Hz seriesof GPS sensors, other industry-equivalent navigation sensors and/orsystems, and may perform navigational and/or geolocation functions usingany known or future-developed standard and/or architecture.

The orientation sensor 312 may include one or more sensors configured todetermine an orientation of the vehicle 100 relative to at least onereference point. In some embodiments, the orientation sensor 312 mayinclude at least one pressure transducer, stress/strain gauge,accelerometer, gyroscope, and/or geomagnetic sensor. Examples of thenavigation sensor 308 as described herein may include, but are notlimited to, at least one of Bosch Sensortec BMX 160 series low-powerabsolute orientation sensors, Bosch Sensortec BMX055 9-axis sensors,Bosch Sensortec BMI055 6-axis inertial sensors, Bosch Sensortec BMI1606-axis inertial sensors, Bosch Sensortec BMF055 9-axis inertial sensors(accelerometer, gyroscope, and magnetometer) with integrated Cortex M0+microcontroller, Bosch Sensortec BMP280 absolute barometric pressuresensors, Infineon TLV493D-A1B6 3D magnetic sensors, InfineonTLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D magneticsensors, Murata Electronics SCC2000 series combined gyro sensor andaccelerometer, Murata Electronics SCC1300 series combined gyro sensorand accelerometer, other industry-equivalent orientation sensors and/orsystems, and may perform orientation detection and/or determinationfunctions using any known or future-developed standard and/orarchitecture.

The odometry sensor and/or system 316 may include one or more componentsthat is configured to determine a change in position of the vehicle 100over time. In some embodiments, the odometry system 316 may utilize datafrom one or more other sensors and/or systems 304 in determining aposition (e.g., distance, location, etc.) of the vehicle 100 relative toa previously measured position for the vehicle 100. Additionally oralternatively, the odometry sensors 316 may include one or moreencoders, Hall speed sensors, and/or other measurement sensors/devicesconfigured to measure a wheel speed, rotation, and/or number ofrevolutions made over time. Examples of the odometry sensor/system 316as described herein may include, but are not limited to, at least one ofInfineon TLE4924/26/27/28C high-performance speed sensors, InfineonTL4941plusC(B) single chip differential Hall wheel-speed sensors,Infineon TL5041plusC Giant Mangnetoresistance (GMR) effect sensors,Infineon TL family of magnetic sensors, EPC Model 25SP Accu-CoderPro™incremental shaft encoders, EPC Model 30M compact incremental encoderswith advanced magnetic sensing and signal processing technology, EPCModel 925 absolute shaft encoders, EPC Model 958 absolute shaftencoders, EPC Model MA36S/MA63S/SA36S absolute shaft encoders, Dynapar™F18 commutating optical encoder, Dynapar™ HS35R family of phased arrayencoder sensors, other industry-equivalent odometry sensors and/orsystems, and may perform change in position detection and/ordetermination functions using any known or future-developed standardand/or architecture.

The LIDAR sensor/system 320 may include one or more componentsconfigured to measure distances to targets using laser illumination. Insome embodiments, the LIDAR sensor/system 320 may provide 3D imagingdata of an environment around the vehicle 100. The imaging data may beprocessed to generate a full 360-degree view of the environment aroundthe vehicle 100. The LIDAR sensor/system 320 may include a laser lightgenerator configured to generate a plurality of target illuminationlaser beams (e.g., laser light channels). In some embodiments, thisplurality of laser beams may be aimed at, or directed to, a rotatingreflective surface (e.g., a mirror) and guided outwardly from the LIDARsensor/system 320 into a measurement environment. The rotatingreflective surface may be configured to continually rotate 360 degreesabout an axis, such that the plurality of laser beams is directed in afull 360-degree range around the vehicle 100. A photodiode receiver ofthe LIDAR sensor/system 320 may detect when light from the plurality oflaser beams emitted into the measurement environment returns (e.g.,reflected echo) to the LIDAR sensor/system 320. The LIDAR sensor/system320 may calculate, based on a time associated with the emission of lightto the detected return of light, a distance from the vehicle 100 to theilluminated target. In some embodiments, the LIDAR sensor/system 320 maygenerate over 2.0 million points per second and have an effectiveoperational range of at least 100 meters. Examples of the LIDARsensor/system 320 as described herein may include, but are not limitedto, at least one of Velodyne® LiDARTM HDL-64E 64-channel LIDAR sensors,Velodyne® LiDAR™ HDL-32E 32-channel LIDAR sensors, Velodyne® LiDAR™PUCK™ VLP-16 16-channel LIDAR sensors, Leica Geosystems Pegasus:Twomobile sensor platform, Garmin® LIDAR-Lite v3 measurement sensor,Quanergy M8 LiDAR sensors, Quanergy S3 solid state LiDAR sensor,LeddarTech® LeddarVU compact solid state fixed-beam LIDAR sensors, otherindustry-equivalent LIDAR sensors and/or systems, and may performilluminated target and/or obstacle detection in an environment aroundthe vehicle 100 using any known or future-developed standard and/orarchitecture.

The RADAR sensors 324 may include one or more radio components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the RADAR sensors 324 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The RADAR sensors 324 may include atransmitter configured to generate and emit electromagnetic waves (e.g.,radio, microwaves, etc.) and a receiver configured to detect returnedelectromagnetic waves. In some embodiments, the RADAR sensors 324 mayinclude at least one processor configured to interpret the returnedelectromagnetic waves and determine locational properties of targets.Examples of the RADAR sensors 324 as described herein may include, butare not limited to, at least one of Infineon RASIC™ RTN7735PLtransmitter and RRN7745PL/46PL receiver sensors, Autoliv ASP VehicleRADAR sensors, Delphi L2C0051TR 77GHz ESR Electronically Scanning Radarsensors, Fujitsu Ten Ltd. Automotive Compact 77GHz 3D Electronic ScanMillimeter Wave Radar sensors, other industry-equivalent RADAR sensorsand/or systems, and may perform radio target and/or obstacle detectionin an environment around the vehicle 100 using any known orfuture-developed standard and/or architecture.

The ultrasonic sensors 328 may include one or more components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the ultrasonic sensors 328 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The ultrasonic sensors 328 mayinclude an ultrasonic transmitter and receiver, or transceiver,configured to generate and emit ultrasound waves and interpret returnedechoes of those waves. In some embodiments, the ultrasonic sensors 328may include at least one processor configured to interpret the returnedultrasonic waves and determine locational properties of targets.Examples of the ultrasonic sensors 328 as described herein may include,but are not limited to, at least one of Texas Instruments TIDA-00151automotive ultrasonic sensor interface IC sensors, MaxBotix® MB8450ultrasonic proximity sensor, MaxBotix® ParkSonar™-EZ ultrasonicproximity sensors, Murata Electronics MA40H1S-R open-structureultrasonic sensors, Murata Electronics MA40S4R/S open-structureultrasonic sensors, Murata Electronics MA58MF14-7N waterproof ultrasonicsensors, other industry-equivalent ultrasonic sensors and/or systems,and may perform ultrasonic target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

The camera sensors 332 may include one or more components configured todetect image information associated with an environment of the vehicle100. In some embodiments, the camera sensors 332 may include a lens,filter, image sensor, and/or a digital image processer. It is an aspectof the present disclosure that multiple camera sensors 332 may be usedtogether to generate stereo images providing depth measurements.Examples of the camera sensors 332 as described herein may include, butare not limited to, at least one of ON Semiconductor® MT9V024 GlobalShutter VGA GS CMOS image sensors, Teledyne DALSA Falcon2 camerasensors, CMOSIS CMV50000 high-speed CMOS image sensors, otherindustry-equivalent camera sensors and/or systems, and may performvisual target and/or obstacle detection in an environment around thevehicle 100 using any known or future-developed standard and/orarchitecture.

The infrared (IR) sensors 336 may include one or more componentsconfigured to detect image information associated with an environment ofthe vehicle 100. The IR sensors 336 may be configured to detect targetsin low-light, dark, or poorly-lit environments. The IR sensors 336 mayinclude an IR light emitting element (e.g., IR light emitting diode(LED), etc.) and an IR photodiode. In some embodiments, the IRphotodiode may be configured to detect returned IR light at or about thesame wavelength to that emitted by the IR light emitting element. Insome embodiments, the IR sensors 336 may include at least one processorconfigured to interpret the returned IR light and determine locationalproperties of targets. The IR sensors 336 may be configured to detectand/or measure a temperature associated with a target (e.g., an object,pedestrian, other vehicle, etc.). Examples of IR sensors 336 asdescribed herein may include, but are not limited to, at least one ofOpto Diode lead-salt IR array sensors, Opto Diode OD-850 Near-IR LEDsensors, Opto Diode SA/SHA727 steady state IR emitters and IR detectors,FLIR® LS microbolometer sensors, FLIR® TacFLIR 380-HD InSb MWIR FPA andHD MWIR thermal sensors, FLIR® VOx 640×480 pixel detector sensors,Delphi IR sensors, other industry-equivalent IR sensors and/or systems,and may perform IR visual target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

In some embodiments, the driving vehicle sensors and systems 304 mayinclude other sensors 338 and/or combinations of the sensors 308-336described above. Additionally or alternatively, one or more of thesensors 308-336 described above may include one or more processorsconfigured to process and/or interpret signals detected by the one ormore sensors 308-336. In some embodiments, the processing of at leastsome sensor information provided by the vehicle sensors and systems 304may be processed by at least one sensor processor 340. Raw and/orprocessed sensor data may be stored in a sensor data memory 344 storagemedium. In some embodiments, the sensor data memory 344 may storeinstructions used by the sensor processor 340 for processing sensorinformation provided by the sensors and systems 304. In any event, thesensor data memory 344 may be a disk drive, optical storage device,solid-state storage device such as a random access memory (“RAM”) and/ora read-only memory (“ROM”), which can be programmable, flash-updateable,and/or the like.

The vehicle control system 348 may receive processed sensor informationfrom the sensor processor 340 and determine to control an aspect of thevehicle 100. Controlling an aspect of the vehicle 100 may includepresenting information via one or more display devices 372 associatedwith the vehicle, sending commands to one or more computing devices 368associated with the vehicle, and/or controlling a driving operation ofthe vehicle. In some embodiments, the vehicle control system 348 maycorrespond to one or more computing systems that control drivingoperations of the vehicle 100 in accordance with the Levels of drivingautonomy described above. In one embodiment, the vehicle control system348 may operate a speed of the vehicle 100 by controlling an outputsignal to the accelerator and/or braking system of the vehicle. In thisexample, the vehicle control system 348 may receive sensor datadescribing an environment surrounding the vehicle 100 and, based on thesensor data received, determine to adjust the acceleration, poweroutput, and/or braking of the vehicle 100. The vehicle control system348 may additionally control steering and/or other driving functions ofthe vehicle 100.

The vehicle control system 348 may communicate, in real-time, with thedriving sensors and systems 304 forming a feedback loop. In particular,upon receiving sensor information describing a condition of targets inthe environment surrounding the vehicle 100, the vehicle control system348 may autonomously make changes to a driving operation of the vehicle100. The vehicle control system 348 may then receive subsequent sensorinformation describing any change to the condition of the targetsdetected in the environment as a result of the changes made to thedriving operation. This continual cycle of observation (e.g., via thesensors, etc.) and action (e.g., selected control or non-control ofvehicle operations, etc.) allows the vehicle 100 to operate autonomouslyin the environment.

In some embodiments, the one or more components of the vehicle 100(e.g., the driving vehicle sensors 304, vehicle control system 348,display devices 372, etc.) may communicate across the communicationnetwork 352 to one or more entities 356A-N via a communicationssubsystem 350 of the vehicle 100. Embodiments of the communicationssubsystem 350 are described in greater detail in conjunction with FIG.5. For instance, the navigation sensors 308 may receive globalpositioning, location, and/or navigational information from a navigationsource 356A. In some embodiments, the navigation source 356A may be aglobal navigation satellite system (GNSS) similar, if not identical, toNAVSTAR GPS, GLONASS, EU Galileo, and/or the BeiDou Navigation SatelliteSystem (BDS) to name a few.

In some embodiments, the vehicle control system 348 may receive controlinformation from one or more control sources 356B. The control source356 may provide vehicle control information including autonomous drivingcontrol commands, vehicle operation override control commands, and thelike. The control source 356 may correspond to an autonomous vehiclecontrol system, a traffic control system, an administrative controlentity, and/or some other controlling server. It is an aspect of thepresent disclosure that the vehicle control system 348 and/or othercomponents of the vehicle 100 may exchange communications with thecontrol source 356 across the communication network 352 and via thecommunications subsystem 350.

Information associated with controlling driving operations of thevehicle 100 may be stored in a control data memory 364 storage medium.The control data memory 364 may store instructions used by the vehiclecontrol system 348 for controlling driving operations of the vehicle100, historical control information, autonomous driving control rules,and the like. In some embodiments, the control data memory 364 may be adisk drive, optical storage device, solid-state storage device such as arandom access memory (“RAM”) and/or a read-only memory (“ROM”), whichcan be programmable, flash-updateable, and/or the like.

In addition to the mechanical components described herein, the vehicle100 may include a number of user interface devices. The user interfacedevices receive and translate human input into a mechanical movement orelectrical signal or stimulus. The human input may be one or more ofmotion (e.g., body movement, body part movement, in two-dimensional orthree-dimensional space, etc.), voice, touch, and/or physicalinteraction with the components of the vehicle 100. In some embodiments,the human input may be configured to control one or more functions ofthe vehicle 100 and/or systems of the vehicle 100 described herein. Userinterfaces may include, but are in no way limited to, at least onegraphical user interface of a display device, steering wheel ormechanism, transmission lever or button (e.g., including park, neutral,reverse, and/or drive positions, etc.), throttle control pedal ormechanism, brake control pedal or mechanism, power control switch,communications equipment, etc.

FIG. 4 shows one embodiment of the instrument panel 400 of the vehicle100. The instrument panel 400 of vehicle 100 comprises a steering wheel410, a vehicle operational display 420 (e.g., configured to presentand/or display driving data such as speed, measured air resistance,vehicle information, entertainment information, etc.), one or moreauxiliary displays 424 (e.g., configured to present and/or displayinformation segregated from the operational display 420, entertainmentapplications, movies, music, etc.), a heads-up display 434 (e.g.,configured to display any information previously described including,but in no way limited to, guidance information such as route todestination, or obstacle warning information to warn of a potentialcollision, or some or all primary vehicle operational data such asspeed, resistance, etc.), a power management display 428 (e.g.,configured to display data corresponding to electric power levels ofvehicle 100, reserve power, charging status, etc.), and an input device432 (e.g., a controller, touchscreen, or other interface deviceconfigured to interface with one or more displays in the instrumentpanel or components of the vehicle 100. The input device 432 may beconfigured as a joystick, mouse, touchpad, tablet, 3D gesture capturedevice, etc.). In some embodiments, the input device 432 may be used tomanually maneuver a portion of the vehicle 100 into a charging position(e.g., moving a charging plate to a desired separation distance, etc.).

While one or more of displays of instrument panel 400 may betouch-screen displays, it should be appreciated that the vehicleoperational display may be a display incapable of receiving touch input.For instance, the operational display 420 that spans across an interiorspace centerline 404 and across both a first zone 408A and a second zone408B may be isolated from receiving input from touch, especially from apassenger. In some cases, a display that provides vehicle operation orcritical systems information and interface may be restricted fromreceiving touch input and/or be configured as a non-touch display. Thistype of configuration can prevent dangerous mistakes in providing touchinput where such input may cause an accident or unwanted control.

In some embodiments, one or more displays of the instrument panel 400may be mobile devices and/or applications residing on a mobile devicesuch as a smart phone. Additionally or alternatively, any of theinformation described herein may be presented to one or more portions420A-N of the operational display 420 or other display 424, 428, 434. Inone embodiment, one or more displays of the instrument panel 400 may bephysically separated or detached from the instrument panel 400. In somecases, a detachable display may remain tethered to the instrument panel.

The portions 420A-N of the operational display 420 may be dynamicallyreconfigured and/or resized to suit any display of information asdescribed. Additionally or alternatively, the number of portions 420A-Nused to visually present information via the operational display 420 maybe dynamically increased or decreased as required, and are not limitedto the configurations shown.

FIG. 5 illustrates a hardware diagram of communications componentry thatcan be optionally associated with the vehicle 100 in accordance withembodiments of the present disclosure.

The communications componentry can include one or more wired or wirelessdevices such as a transceiver(s) and/or modem that allows communicationsnot only between the various systems disclosed herein but also withother devices, such as devices on a network, and/or on a distributednetwork such as the Internet and/or in the cloud and/or with othervehicle(s).

The communications subsystem 350 can also include inter- andintra-vehicle communications capabilities such as hotspot and/or accesspoint connectivity for any one or more of the vehicle occupants and/orvehicle-to-vehicle communications.

Additionally, and while not specifically illustrated, the communicationssubsystem 350 can include one or more communications links (that can bewired or wireless) and/or communications busses (managed by the busmanager 574), including one or more of CANbus, OBD-II, ARCINC 429,Byteflight, CAN (Controller Area Network), D2B (Domestic Digital Bus),FlexRay, DC-BUS, IDB-1394, IEBus, I2C, ISO 9141-1/-2, J1708, J1587,J1850, J1939, ISO 11783, Keyword Protocol 2000, LIN (Local InterconnectNetwork), MOST (Media Oriented Systems Transport), Multifunction VehicleBus, SMARTwireX, SPI, VAN (Vehicle Area Network), and the like or ingeneral any communications protocol and/or standard(s).

The various protocols and communications can be communicated one or moreof wirelessly and/or over transmission media such as single wire,twisted pair, fiber optic, IEEE 1394, MIL-STD-1553, MIL-STD-1773,power-line communication, or the like. (All of the above standards andprotocols are incorporated herein by reference in their entirety).

As discussed, the communications subsystem 350 enables communicationsbetween any if the inter-vehicle systems and subsystems as well ascommunications with non-collocated resources, such as those reachableover a network such as the Internet.

The communications subsystem 350, in addition to well-known componentry(which has been omitted for clarity), includes interconnected elementsincluding one or more of: one or more antennas 504, aninterleaver/deinterleaver 508, an analog front end (AFE) 512,memory/storage/cache 516, controller/microprocessor 520, MAC circuitry522, modulator/demodulator 524, encoder/decoder 528, a plurality ofconnectivity managers 534, 558, 562, 566, GPU 540, accelerator 544, amultiplexer/demultiplexer 552, transmitter 570, receiver 572 andwireless radio 578 components such as a Wi-Fi PHY/Bluetooth® module 580,a Wi-Fi/BT MAC module 584, transmitter 588 and receiver 592. The variouselements in the device 350 are connected by one or more links/busses 5(not shown, again for sake of clarity).

The device 350 can have one more antennas 504, for use in wirelesscommunications such as multi-input multi-output (MIMO) communications,multi-user multi-input multi-output (MU-MIMO) communications Bluetooth®,LTE, 4G, 5G, Near-Field Communication (NFC), etc., and in general forany type of wireless communications. The antenna(s) 504 can include, butare not limited to one or more of directional antennas, omnidirectionalantennas, monopoles, patch antennas, loop antennas, microstrip antennas,dipoles, and any other antenna(s) suitable for communicationtransmission/reception. In an exemplary embodiment,transmission/reception using MIMO may require particular antennaspacing. In another exemplary embodiment, MIMO transmission/receptioncan enable spatial diversity allowing for different channelcharacteristics at each of the antennas. In yet another embodiment, MIMOtransmission/reception can be used to distribute resources to multipleusers for example within the vehicle 100 and/or in another vehicle.

Antenna(s) 504 generally interact with the Analog Front End (AFE) 512,which is needed to enable the correct processing of the receivedmodulated signal and signal conditioning for a transmitted signal. TheAFE 512 can be functionally located between the antenna and a digitalbaseband system in order to convert the analog signal into a digitalsignal for processing and vice-versa.

The subsystem 350 can also include a controller/microprocessor 520 and amemory/storage/cache 516. The subsystem 350 can interact with thememory/storage/cache 516 which may store information and operationsnecessary for configuring and transmitting or receiving the informationdescribed herein. The memory/storage/cache 516 may also be used inconnection with the execution of application programming or instructionsby the controller/microprocessor 520, and for temporary or long termstorage of program instructions and/or data. As examples, thememory/storage/cache 520 may comprise a computer-readable device, RAM,ROM, DRAM, SDRAM, and/or other storage device(s) and media.

The controller/microprocessor 520 may comprise a general purposeprogrammable processor or controller for executing applicationprogramming or instructions related to the subsystem 350. Furthermore,the controller/microprocessor 520 can perform operations for configuringand transmitting/receiving information as described herein. Thecontroller/microprocessor 520 may include multiple processor cores,and/or implement multiple virtual processors. Optionally, thecontroller/microprocessor 520 may include multiple physical processors.By way of example, the controller/microprocessor 520 may comprise aspecially configured Application Specific Integrated Circuit (ASIC) orother integrated circuit, a digital signal processor(s), a controller, ahardwired electronic or logic circuit, a programmable logic device orgate array, a special purpose computer, or the like.

The subsystem 350 can further include a transmitter 570 and receiver 572which can transmit and receive signals, respectively, to and from otherdevices, subsystems and/or other destinations using the one or moreantennas 504 and/or links/busses. Included in the subsystem 350circuitry is the medium access control or MAC Circuitry 522. MACcircuitry 522 provides for controlling access to the wireless medium. Inan exemplary embodiment, the MAC circuitry 522 may be arranged tocontend for the wireless medium and configure frames or packets forcommunicating over the wired/wireless medium.

The subsystem 350 can also optionally contain a security module (notshown). This security module can contain information regarding but notlimited to, security parameters required to connect the device to one ormore other devices or other available network(s), and can include WEP orWPA/WPA-2 (optionally+AES and/or TKIP) security access keys, networkkeys, etc. The WEP security access key is a security password used byWi-Fi networks. Knowledge of this code can enable a wireless device toexchange information with an access point and/or another device. Theinformation exchange can occur through encoded messages with the WEPaccess code often being chosen by the network administrator. WPA is anadded security standard that is also used in conjunction with networkconnectivity with stronger encryption than WEP.

In some embodiments, the communications subsystem 350 also includes aGPU 540, an accelerator 544, a Wi-Fi/BT/BLE PHY module 580 and aWi-Fi/BT/BLE MAC module 584 and wireless transmitter 588 and receiver592. In some embodiments, the GPU 540 may be a graphics processing unit,or visual processing unit, comprising at least one circuit and/or chipthat manipulates and changes memory to accelerate the creation of imagesin a frame buffer for output to at least one display device. The GPU 540may include one or more of a display device connection port, printedcircuit board (PCB), a GPU chip, a metal-oxide-semiconductorfield-effect transistor (MOSFET), memory (e.g., single data raterandom-access memory (SDRAM), double data rate random-access memory(DDR) RAM, etc., and/or combinations thereof), a secondary processingchip (e.g., handling video out capabilities, processing, and/or otherfunctions in addition to the GPU chip, etc.), a capacitor, heatsink,temperature control or cooling fan, motherboard connection, shielding,and the like.

The various connectivity managers 534, 558, 562, 566 manage and/orcoordinate communications between the subsystem 350 and one or more ofthe systems disclosed herein and one or more other devices/systems. Theconnectivity managers 534, 558, 562, 566 include a charging connectivitymanager 534, a vehicle database connectivity manager 558, a remoteoperating system connectivity manager 562, and a sensor connectivitymanager 566.

The charging connectivity manager 534 can coordinate not only thephysical connectivity between the vehicle 100 and a chargingdevice/vehicle, but can also communicate with one or more of a powermanagement controller, one or more third parties and optionally abilling system(s). As an example, the vehicle 100 can establishcommunications with the charging device/vehicle to one or more ofcoordinate interconnectivity between the two (e.g., by spatiallyaligning the charging receptacle on the vehicle with the charger on thecharging vehicle) and optionally share navigation information. Oncecharging is complete, the amount of charge provided can be tracked andoptionally forwarded to, for example, a third party for billing. Inaddition to being able to manage connectivity for the exchange of power,the charging connectivity manager 534 can also communicate information,such as billing information to the charging vehicle and/or a thirdparty. This billing information could be, for example, the owner of thevehicle, the driver/occupant(s) of the vehicle, company information, orin general any information usable to charge the appropriate entity forthe power received.

The vehicle database connectivity manager 558 allows the subsystem toreceive and/or share information stored in the vehicle database. Thisinformation can be shared with other vehicle components/subsystemsand/or other entities, such as third parties and/or charging systems.The information can also be shared with one or more vehicle occupantdevices, such as an app (application) on a mobile device the driver usesto track information about the vehicle 100 and/or a dealer orservice/maintenance provider. In general any information stored in thevehicle database can optionally be shared with any one or more otherdevices optionally subject to any privacy or confidentiallyrestrictions.

The remote operating system connectivity manager 562 facilitatescommunications between the vehicle 100 and any one or more autonomousvehicle systems. These communications can include one or more ofnavigation information, vehicle information, other vehicle information,weather information, occupant information, or in general any informationrelated to the remote operation of the vehicle 100.

The sensor connectivity manager 566 facilitates communications betweenany one or more of the vehicle sensors (e.g., the driving vehiclesensors and systems 304, etc.) and any one or more of the other vehiclesystems. The sensor connectivity manager 566 can also facilitatecommunications between any one or more of the sensors and/or vehiclesystems and any other destination, such as a service company, app, or ingeneral to any destination where sensor data is needed.

In accordance with one exemplary embodiment, any of the communicationsdiscussed herein can be communicated via the conductor(s) used forcharging. One exemplary protocol usable for these communications isPower-line communication (PLC). PLC is a communication protocol thatuses electrical wiring to simultaneously carry both data, andAlternating Current (AC) electric power transmission or electric powerdistribution. It is also known as power-line carrier, power-line digitalsubscriber line (PDSL), mains communication, power-linetelecommunications, or power-line networking (PLN). For DC environmentsin vehicles PLC can be used in conjunction with CAN-bus, LIN-bus overpower line (DC-LIN) and DC-BUS.

The communications subsystem can also optionally manage one or moreidentifiers, such as an IP (internet protocol) address(es), associatedwith the vehicle and one or other system or subsystems or componentstherein. These identifiers can be used in conjunction with any one ormore of the connectivity managers as discussed herein.

FIG. 6 illustrates a block diagram of a computing environment 600 thatmay function as the servers, user computers, or other systems providedand described herein. The computing environment 600 includes one or moreuser computers, or computing devices, such as a vehicle computing device604, a communication device 608, and/or more 612. The computing devices604, 608, 612 may include general purpose personal computers (including,merely by way of example, personal computers, and/or laptop computersrunning various versions of Microsoft Corp.'s Windows® and/or AppleCorp.'s Macintosh® operating systems) and/or workstation computersrunning any of a variety of commercially-available UNIX® or UNIX-likeoperating systems. These computing devices 604, 608, 612 may also haveany of a variety of applications, including for example, database clientand/or server applications, and web browser applications. Alternatively,the computing devices 604, 608, 612 may be any other electronic device,such as a thin-client computer, Internet-enabled mobile telephone,and/or personal digital assistant, capable of communicating via anetwork 352 and/or displaying and navigating web pages or other types ofelectronic documents. Although the exemplary computing environment 600is shown with two computing devices, any number of user computers orcomputing devices may be supported.

The computing environment 600 may also include one or more servers 614,616. In this example, server 614 is shown as a web server and server 616is shown as an application server. The web server 614, which may be usedto process requests for web pages or other electronic documents fromcomputing devices 604, 608, 612. The web server 614 can be running anoperating system including any of those discussed above, as well as anycommercially-available server operating systems. The web server 614 canalso run a variety of server applications, including SIP (SessionInitiation Protocol) servers, HTTP(s) servers, FTP servers, CGI servers,database servers, Java servers, and the like. In some instances, the webserver 614 may publish operations available operations as one or moreweb services.

The computing environment 600 may also include one or more file andor/application servers 616, which can, in addition to an operatingsystem, include one or more applications accessible by a client runningon one or more of the computing devices 604, 608, 612. The server(s) 616and/or 614 may be one or more general purpose computers capable ofexecuting programs or scripts in response to the computing devices 604,608, 612. As one example, the server 616, 614 may execute one or moreweb applications. The web application may be implemented as one or morescripts or programs written in any programming language, such as Java™,C, C#®, or C++, and/or any scripting language, such as Perl, Python, orTCL, as well as combinations of any programming/scripting languages. Theapplication server(s) 616 may also include database servers, includingwithout limitation those commercially available from Oracle®,Microsoft®, Sybase®, IBM® and the like, which can process requests fromdatabase clients running on a computing device 604, 608, 612.

The web pages created by the server 614 and/or 616 may be forwarded to acomputing device 604, 608, 612 via a web (file) server 614, 616.Similarly, the web server 614 may be able to receive web page requests,web services invocations, and/or input data from a computing device 604,608, 612 (e.g., a user computer, etc.) and can forward the web pagerequests and/or input data to the web (application) server 616. Infurther embodiments, the server 616 may function as a file server.Although for ease of description, FIG. 6 illustrates a separate webserver 614 and file/application server 616, those skilled in the artwill recognize that the functions described with respect to servers 614,616 may be performed by a single server and/or a plurality ofspecialized servers, depending on implementation-specific needs andparameters. The computer systems 604, 608, 612, web (file) server 614and/or web (application) server 616 may function as the system, devices,or components described in FIGS. 1-6.

The computing environment 600 may also include a database 618. Thedatabase 618 may reside in a variety of locations. By way of example,database 618 may reside on a storage medium local to (and/or residentin) one or more of the computers 604, 608, 612, 614, 616. Alternatively,it may be remote from any or all of the computers 604, 608, 612, 614,616, and in communication (e.g., via the network 610) with one or moreof these. The database 618 may reside in a storage-area network (“SAN”)familiar to those skilled in the art. Similarly, any necessary files forperforming the functions attributed to the computers 604, 608, 612, 614,616 may be stored locally on the respective computer and/or remotely, asappropriate. The database 618 may be a relational database, such asOracle 20i®, that is adapted to store, update, and retrieve data inresponse to SQL-formatted commands.

FIG. 7 illustrates one embodiment of a computer system 700 upon whichthe servers, user computers, computing devices, or other systems orcomponents described above may be deployed or executed. The computersystem 700 is shown comprising hardware elements that may beelectrically coupled via a bus 704. The hardware elements may includeone or more central processing units (CPUs) 708; one or more inputdevices 712 (e.g., a mouse, a keyboard, etc.); and one or more outputdevices 716 (e.g., a display device, a printer, etc.). The computersystem 700 may also include one or more storage devices 720. By way ofexample, storage device(s) 720 may be disk drives, optical storagedevices, solid-state storage devices such as a random access memory(“RAM”) and/or a read-only memory (“ROM”), which can be programmable,flash-updateable and/or the like.

The computer system 700 may additionally include a computer-readablestorage media reader 724; a communications system 728 (e.g., a modem, anetwork card (wireless or wired), an infra-red communication device,etc.); and working memory 736, which may include RAM and ROM devices asdescribed above. The computer system 700 may also include a processingacceleration unit 732, which can include a DSP, a special-purposeprocessor, and/or the like.

The computer-readable storage media reader 724 can further be connectedto a computer-readable storage medium, together (and, optionally, incombination with storage device(s) 720) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containingcomputer-readable information. The communications system 728 may permitdata to be exchanged with a network and/or any other computer describedabove with respect to the computer environments described herein.Moreover, as disclosed herein, the term “storage medium” may representone or more devices for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information.

The computer system 700 may also comprise software elements, shown asbeing currently located within a working memory 736, including anoperating system 740 and/or other code 744. It should be appreciatedthat alternate embodiments of a computer system 700 may have numerousvariations from that described above. For example, customized hardwaremight also be used and/or particular elements might be implemented inhardware, software (including portable software, such as applets), orboth. Further, connection to other computing devices such as networkinput/output devices may be employed.

Examples of the processors 340, 708 as described herein may include, butare not limited to, at least one of Qualcomm® Snapdragon® 800 and 801,Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bitcomputing, Apple® A7 processor with 64-bit architecture, Apple® M7motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family ofprocessors, the Intel® Xeon® family of processors, the Intel® Atom™family of processors, the Intel Itanium® family of processors, Intel®Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nmIvy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300,and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments®Jacinto C6000™ automotive infotainment processors, Texas Instruments®OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors,ARM® Cortex-A and ARM926EJ-STM processors, other industry-equivalentprocessors, and may perform computational functions using any known orfuture-developed standard, instruction set, libraries, and/orarchitecture.

Embodiments can provide an intelligent autonomous vehicle control systemthat augments the learned behaviors from autonomous vehicle drivingoperations with temporally, spatially, or event-limited identifiedbehaviors and other autonomous driving information received from theintelligent autonomous vehicle control system. The temporally,spatially, or event-limited identified behaviors and other autonomousdriving information can be learned by the control system monitoring thebehaviors of multiple autonomous vehicles or other objects whosebehavior is to be modeled by passing autonomous vehicles. The behaviorscan be published by the control source by being pushed to selectedvehicles and/or attached to navigation information. In this way, thelearned behaviors of multiple vehicles can be shared via the controlsource, thereby decreasing accident rates and increasing vehicle safety.

The intelligent autonomous vehicle control system can embed identifiedautonomous driving information, such as commands, requests, warnings,logic, instructions, rules, references, identifiers, observed drivingbehaviors, or links to locally or remote stored autonomous drivingrules, logic or instructions, in the navigation information provided bythe navigation source 356A. A flag can be included in the navigationinformation to indicate the existence of such embedded identifiedautonomous driving information.

A similar ad hoc network can be formed for the purpose of transmittingcollected information to the navigation source 356A or control source356B. A designated or master vehicle in the ad hoc network can transmitcertain types collected information common to multiple vehicles in thenetwork, such as sensed object information and environmental informationthat are substantially duplicative from vehicle-to-vehicle, to thenavigation source 356A or control source 356B while uncommon types ofcollected information, such as sensed occupant information andvehicle-related information that are not duplicative vehicle-to-vehicle,are transmitted by each vehicle separately.

The intelligent autonomous vehicle control system (e.g., the mastervehicle) can form an ad hoc wireless network with surrounding vehicles(e.g., the slave vehicles) and provide the embedded identifiedautonomous driving information to the networked vehicles. This canreduce significantly the bandwidth requirements to provide by thecontrol source 356B to provide navigation information and embeddedidentified autonomous driving information to each of the surroundingvehicles. Membership of the ad hoc wireless network and member role canchange dynamically as vehicles move in and out of proximity to eachother.

With reference to FIGS. 3 and 8-9, the vehicle 100 is in wirelesscommunication, via network 352, with navigation source 356A comprising amap database manager 812 and associated map database 816 and the controlsource 356B having an associated control source database 824.

The map database manager 812 and map database 816 interact with thenavigation sensor 308 (which is part of the automatic vehicle locationsystem 908 discussed below) in the vehicle 100 to provide navigation ormap output to an autonomous driving agent 904 in the vehicle 100.

The map database manager 812 stores and recalls navigation informationfrom the map database 816.

Maps are commonly stored as graphs, or two or three dimensional arraysof objects with attributes of location and category, where some commoncategories include parks, roads, cities, and the like. A map databasecommonly represents a road network along with associated features, withthe road network corresponding to a selected road network model.Commonly, such a model comprises basic elements (nodes, links and areas)of the road network and properties of those elements (locationcoordinates, shape, addresses, road class, speed range, etc.). The basicelements are referred to as features and the properties as attributes.Other information associated with the road network can also be included,such as points of interest, waypoints, building shapes, and politicalboundaries. Geographic Data Files (GDF) is a standardized description ofsuch a model. Each node within a map graph represents a point locationof the surface of the Earth and can be represented by a pair oflongitude (lon) and latitude (lat) coordinates. Each link can representa stretch of road between two nodes, and be represented by a linesegment (corresponding to a straight section of road) or a curve havinga shape that is generally described by intermediate points (called shapepoints) along the link. However, curves can also be represented by acombination of centroid (point or node), with a radius, and polarcoordinates to define the boundaries of the curve. Shape points can berepresented by longitude and latitude coordinates as are nodes, butshape points generally do not serve the purpose of connecting links, asdo nodes. Areas are generally two- or three-dimensional shapes thatrepresent things like parks, cities, blocks and are defined by theirboundaries (usually formed by a closed polygon).

Auxiliary data can be attached by the map database manager 812 to thefeatures and/or attributes. The auxiliary data can be not only variousnavigational functions, involving active safety, and driver assistancebut also identified autonomous driving information relating to anautonomous vehicle or other object to be sensed by passing autonomousvehicles, such as observed behaviors of other autonomous vehicles or anobject at the map location, to be applied at the correspondinggeographic locations. The auxiliary data, for example, can compriseidentified embedded autonomous driving information, such as commands tothe receiving autonomous driving agent, requests to the receivingautonomous driving agent, warnings to the receiving autonomous drivingagent, (e.g., of potential hazards such as potholes, hazardous objectsin or near the roadway, poor roadway conditions (such as icy or wet),heavy traffic warning, emergency vehicle or personnel-related warning,vehicle wreck warning, road construction warning, bridge or roadway outwarning, high water or flood warning, and the like) logic, instructionsor rules to be employed by the receiving autonomous driving agent,references, identifiers, observed behaviors, or links to locally orremote stored autonomous driving rules, logic or instructions to beemployed the receiving autonomous driving agent, in the navigationinformation provided by the navigation source 356A.

The identified autonomous driving information embedded in the navigationinformation as auxiliary data can include temporal, spatial, orevent-limitations learned by the control system monitoring the behaviorsof multiple autonomous vehicles. The identified autonomous drivinginformation can be limited in application by temporal limitations (e.g.,identified behavior application start and end times), spatiallimitations (e.g., sets of geographical coordinates defining an area inor location at which the identified autonomous driving information is tobe applied), or event limitations (e.g., a defined event (such as aweather storm event, ambient temperature range (such as below freezing),set of road conditions, etc.) during which the identified autonomousdriving information is to be applied but after which the autonomousdriving information is not to be applied).

The auxiliary data fields can include a flag to indicate the existenceof such identified embedded autonomous driving information relating toan autonomous vehicle or other object to be sensed by passing autonomousvehicles. When the flag is set, the autonomous vehicle driving agentaccesses the field(s) dedicated to identified embedded autonomousdriving information and, when the flag is not set, the autonomousvehicle driving agent does not access the field(s) as they are deemednot to contain identified autonomous driving information.

The functions and other auxiliary data can be cross-referenced with theentities and attributes of the main map database 816. Since theauxiliary data is not necessarily compiled with the main map database816 some other means is generally needed to establish cross-referencing,or attaching of the auxiliary data. The common approaches arefunction-specific referencing tables and generic referencing.

Function-specific referencing tables provide a technique for attachingfunction-specific data, such as embedded identified autonomous drivinginformation relating to an autonomous vehicle or other object to besensed by passing autonomous vehicles, to the map database 816. Such atable can be collaboratively produced by the navigation source 356A andcontrol source 356B to support a specific function or class of functionsinvolving location-based behaviors or embedded identified autonomousdriving information. It will generally include a list of map elements ofa specific type (e.g., links, intersections, point-of-interestlocations, etc.) along with identifying attributes (e.g., street names,longitude/latitude coordinates, etc.). Additionally, each entry in thetable can be assigned a unique identifier. As a practical matter, theresult will represent a small subset of the elements of the given typethat are available in the map databases and will include those that aremore important to the application area.

Generic referencing attaches data, such as observed behaviors andembedded identified autonomous driving information relating to anautonomous vehicle or other object to be sensed by passing autonomousvehicles, to any map database by discovering reference informationthrough a form of map matching. The function-specific data items can beassigned to elements, such as points, links or areas, that likely onlyapproximate the corresponding map elements in a specific map database816. A search of the map database can be made for the best fit. Toenhance the search process, neighboring elements can be strategicallyappended to each given element to help ensure that the correct solutionis found in each case. For example, if the map element is a linkconnecting two intersections, then one or both cross streets could beappended for the sake of the search thereby making an incorrect matchunlikely.

By way of illustration, the Navigation Data Standard (NDS) is astandardized format for automotive-grade navigation databases. NDS usesthe SQLLite Database File Format. An NDS database can have severalproduct databases, and each product database may be divided further intoupdate regions. This concept supports a flexible and consistentversioning concept for NDS databases and makes it possible to integratedatabases from different database suppliers into one NDS database. Theinner structure of databases complying with Navigation Data Standard isfurther characterized by building blocks, levels and the content itself.An update region represents a geographic area in a database that can besubject to an update. All navigation data in an NDS database belongs aspecific building block. Each building block addresses specificfunctional aspects of navigation, such as names for location input,routing, or map display.

Alternatively, the control source 356B can push the identifiedautonomous driving information directly to the autonomous driving agentbased on the selected vehicle location and not incorporate or referencethe identified autonomous driving information in the navigationinformation.

The control source 356B and control source database 824 interact withthe autonomous driving agent 904 in each vehicle 100 to receive varioustypes of information regarding vehicle behavior and the behaviors ofnearby objects, such as other vehicles and pedestrians, identifyspecific behaviors and other autonomous driving information, anddirectly or indirectly provide the autonomous driving information toselected vehicles for use in determining and selecting variousautonomous vehicle commands or settings, particularly acceleration rateof the vehicle, deceleration (e.g., braking) rate of the vehicle,steering angle of the vehicle (e.g., for turns and lane changes), andinter-object spacing (e.g., end-to-end or side-to-side spacing betweenthe vehicle and a nearby object).

The map and control source databases 816 and 824 can be constructedaccording to any data model, whether conceptual, logical, or physical,such as a flat model, hierarchical model, network model, relationalmodel, object-relational model, star schema, entity-relationship model,geographic model, generic model, semantic model, and the like.

Each learned or identified behavior (or other autonomous drivinginformation) is described typically by output behavior and associatedwith a corresponding set of limitations. By way of illustration, theoutput behavior is typically a driving behavior of the car, such as usea specified lane, slow to a selected speed, gently apply brakes, turnlights on, use inter-vehicle spacing of X meters, transition from alower level of automation to a higher level or vice versa, and the like.The learned or identified behavior can be further described withreference to a set of sensed inputs.

The sensed inputs can vary by corresponding object type but include oneor more of geographic or spatial vehicle location, sensed objectinformation 970 (with examples being animate objects such as animals andattributes thereof (e.g., animal type, current spatial location, currentactivity, etc.), and pedestrians and attributes thereof (e.g., identity,age, sex, current spatial location, current activity, etc.), and thelike and inanimate objects and attributes thereof such as other vehicles(e.g., current vehicle state or activity (parked or in motion or levelof automation currently employed), occupant or operator identity,vehicle type (truck, car, etc.), vehicle spatial location, etc.), curbs(topography and spatial location), potholes (size and spatial location),lane division markers (type or color and spatial locations), signage(type or color and spatial locations such as speed limit signs, yieldsigns, stop signs, and other restrictive or warning signs), trafficsignals (e.g., red, yellow, blue, green, etc.), buildings (spatiallocations), walls (height and spatial locations), barricades (height andspatial location), and the like), sensed occupant information 916 (withexamples being number and identities of occupants and attributes thereof(e.g., seating position, age, sex, gaze direction, biometricinformation, authentication information, preferences, historic behaviorpatterns (such as current or historical user driving behavior,historical user route, destination, and waypoint preferences),nationality, ethnicity and race, language preferences (e.g., Spanish,English, Chinese, etc.), current occupant role (e.g., operator orpassenger), occupant priority ranking (e.g., vehicle owner is given ahigher ranking than a child occupant), electronic calendar information(e.g., Outlook™), medical information and history, etc.), selectedvehicle-related information 982 (with examples being vehiclemanufacturer, type, model, year of manufacture, current geographiclocation, current vehicle state or activity (parked or in motion orlevel of automation currently employed), vehicle specifications andcapabilities, currently sensed operational parameters for the vehicle,and other information), exterior environmental information 986 (withexamples being road type (pavement, gravel, brick, etc.), road condition(e.g., wet, dry, icy, snowy, etc.), weather condition (e.g., outsidetemperature, pressure, humidity, wind speed and direction, etc.),ambient light conditions (e.g., time-of-day), degree of development ofvehicle surroundings (e.g., urban or rural), and the like), occupantcommands or other input, and other information.

The identified behavior or other autonomous driving information can bebased on observations of repetitive behavior of multiple vehiclesobserved at a specific map location or area or in response to an event(e.g., any of the sensed object information 970 or sensed environmentalinformation 986) or during a specified time-of-day.

The application or usage of the identified behavior can be limitedtemporally, spatially, or by occurrence or duration of an event. Whilethe application or usage of the identified behavior is permitted by thecorresponding limitation, the identified behavior and other autonomousdriving information is used instead of learned behaviors and otherautonomous driving information of the vehicle. When the application orusage of the identified behavior and other autonomous drivinginformation is not permitted by the corresponding limitation (e.g., thevehicle is outside the spatially limited area, the time duration of thebehavior is expired, or the event has terminated or otherwise ended),the learned behavior and other autonomous driving information of thevehicle is employed.

With reference to FIG. 9, an on board autonomous driving system 900 inthe vehicle 100 is depicted that employs one or more of the foregoingfeatures. The autonomous driving system 900 includes an autonomousdriving agent 904 in communication with an automatic vehicle locationsystem 908, sensor connectivity manager 566 and associated first,second, . . . Mth sensors 912 a-M, user interface 920, andauthentication system 978, and having access via working memory 736 orcommunication systems 728 to the sensed object information 970, sensedoccupant information 916, learned autonomous driving information 974,vehicle-related information 982, exterior environmental information 986,and navigation information 924.

The automatic vehicle location system 908 is in communication with theGPS/Nav sensor 308 to acquire current vehicle position coordinates,which position coordinates are then correlated by the map databasemanager 812 to a position on a road. Dead reckoning using distance datafrom one or more sensors attached to the drive train, a gyroscope sensor312 and/or an accelerometer sensor 312 can be used for greaterreliability, as GPS signal loss and/or multipath can occur due to themap database manager 812, such as due to a cellular signal dead or lowsignal strength area or passage of the vehicle through a tunnel.

The first, second, With sensors 912a-m can collect the sensed objectinformation 970, sensed occupant information 916, vehicle-relatedinformation 982, and exterior environmental information 986. The first,second, . . . mth sensors 912 a-m include the sensors or systems 116A-K,112, 312, 316, 320, 324, 328, 332, 336, and 338 discussed above, acamera to capture images of interior objects (such as occupants), a seatbelt sensor to determine seat belt settings (e.g., closed or open), aseat weight sensor settings, a microphone to capture audio within thevehicle (such as occupant comments which are then input into aspeech-to-text engine to determine or identify one or more words spokenby an occupant), a wireless network node that receives uniqueidentifiers of occupant portable computing devices (which identifierscan be associated with a corresponding occupant to identify theoccupant), and the like. In some applications, a portable computingdevice of the occupant can be employed as a sensor that tracks occupantbehavior while the occupant is in the vehicle. The information collectedby the sensors is received by the sensor connectivity manager 566 andprovided to the autonomous driving agent 904 and/or to the controlsource 356B.

The user interface 920 receives user commands and other input, such asuser selections, preferences, and settings that are used in configuring,determining, and selecting vehicle parameters, settings, or operations,such as navigation route selection, acceptable rates of acceleration anddeceleration, acceptable minimum inter-object spacing distance, andacceptable steering lines, and stimuli or events triggering associatedrule-based actions. The user interface 920 can be one or more of vehicleinstrument panel 400, vehicle operational display 420, heads-up display434, and power management display 428. It can also be a portablecomputational or communication device of an occupant.

The behavior selector 978 determines which behavior logic and otherautonomous driving information is to be employed by the vehicle. Thebehavior selector 978 can determine therefore which locally stored(e.g., in working memory 736) learned behavior or other autonomousdriving information 974 is to be executed or implemented and whichidentified or learned behavior of other autonomous driving informationis to be executed or implemented.

The autonomous driving agent 904 controls the driving behavior of thevehicle, such as whether to execute an accelerate event, accelerationrate, decelerate event, deceleration rate, steering angle selectedrelative to a selected reference axis, and selected inter-object spacingmagnitude in response to the current vehicle location, sensed objectinformation 970, sensed occupant information 916, vehicle-relatedinformation 982, exterior environmental information 986, and navigationinformation 924 in accordance with the autonomous driving informationselected by the behavior selector 978 and implemented by the autonomousdriving agent 904. In a typical implementation, the autonomous drivingagent, based on feedback from certain sensors, specifically the LIDARand radar sensors positioned around the circumference of the vehicle,constructs a three-dimensional map in spatial proximity to the vehiclethat enables the autonomous driving agent to identify and spatiallylocate animate and inanimate objects. Other sensors, such as inertialmeasurement units, gyroscopes, wheel encoders, sonar sensors, motionsensors to perform odometry calculations with respect to nearby movingobjects, and exterior facing cameras (e.g., to perform computer visionprocessing) can provide further contextual information for generation ofa more accurate three-dimensional map. The navigation information iscombined with the three-dimensional map to provide short, intermediateand long range course tracking and route selection. The autonomousdriving system processes real-world information as well as GPS data, anddriving speed to determine accurately the precise position of eachvehicle, down to a few centimeters all while making corrections fornearby animate and inanimate objects.

The autonomous driving agent 904 processes in real time the aggregatemapping information and models behavior of occupants of the currentvehicle and other nearby animate objects relying on the behaviorselector's selected autonomous driving information. The autonomousdriving information can be generically applied to multiple types,models, and manufacturer of vehicles or specific to a specific type,model, or manufacturer of vehicle. The applicability of the respectiveset of identified autonomous driving information can be stored as partof the data structures comprising the identified autonomous drivinginformation.

In some applications, the behavior selector 978 selects between learnedand identified autonomous driving information for a nearby object in thesensed object information 970. The selected autonomous drivinginformation is used to mod& the behavior of the nearby object andtherefore determining a behavior of the selected vehicle to beimplemented by the autonomous driving agent.

The autonomous driving agent, based on the learned and autonomousdriving information, issues appropriate commands regarding implementingan accelerate event, acceleration rate, deceleration event, decelerationrate, inter-object spacing distance, and steering angle magnitude. Whilesome commands are hard-coded into the vehicle, such as stopping at redlights and stop signs, other responses are learned and recorded by thecontrol source or autonomous driving agent based on previous drivingexperiences.

The learning ability of the control source is based on monitoring thebehavior of multiple vehicles and of the autonomous driving agent isbased on monitoring the behavior of the selected vehicle hosting theautonomous driving agent. Examples of learned behavior include aslow-moving or stopped vehicle or emergency vehicle in a right lanesuggests a higher probability that the car following it will attempt topass, a pot hole, rock, or other foreign object in the roadway equatesto a higher probability that a driver will swerve to avoid it, andtraffic congestion in one lane means that other drivers moving in thesame direction will have a higher probability of passing in an adjacentlane or by driving on the shoulder.

As more and more vehicles drive the selected route of the selectedvehicle, sensor collected information of the vehicle can be provided insubstantial real time to the map database manager 812 to enable it togenerate a detailed three-dimensional map as the navigation information.This in effect uses each vehicle as a mapping information source toenable detailed and accurate three dimensional maps to be developed. ifevery vehicle were to provide this collected information, however,bandwidth constraints and limitations would create problems not only fortransmission of the collected information but also for othercommunication services, such as cell phone bandwidth.

One solution is to form ad hoc wireless networks of vehicles to reducethe bandwidth consumed by reporting of the collected information bymultiple nearby vehicles. The ad hoc wireless network can be formed toaccommodate peer-to-peer communications not only for the purpose oftransmitting collected information to but also receiving navigationinformation and autonomous driving information from the navigationsource 356A and/or control source 356B. A designated or master vehiclein the ad hoc network can transmit certain types collected informationcommon to multiple vehicles in the network, such as sensed objectinformation and environmental information that are substantiallyduplicative from vehicle-to-vehicle, to the navigation source 356A orcontrol source 356B while uncommon types of collected information, suchas sensed occupant information and vehicle-related information that arenot duplicative vehicle-to-vehicle, are transmitted by each vehicleseparately. Likewise, the designated or master vehicle in the ad hocnetwork can receive the navigation information or autonomous drivinginformation and transmit all or part of it to multiple vehicles in thenetwork.

As will be appreciated, a wireless ad hoc network (WANET) is adecentralized type of wireless network. It is ad hoc because it does notrely on a pre-existing infrastructure. A wireless ad-hoc network, alsoknown as IBSS—Independent Basic Service Set, is a computer network inwhich the communication links are wireless. The network is ad-hocbecause each node can forward data for other nodes, and so thedetermination of which nodes forward data is made dynamically based on anumber of factors, such as token possession, network connectivity,spatial location, and the like.

By way of example, the wireless ad hoc network can be a vehicular ad hocnetwork or VANET. VANETs can provide communication between vehicles.Intelligent vehicular ad hoc networks (InVANETs) are a kind ofartificial intelligence that helps vehicles to behave in an intelligentmanner or cooperatively. Radio waves can be used to enable vehicleinter-communication but other wireless communication modalities,channels, and protocols can be employed.

The wireless ad hoc network can be based on other mechanisms. such asmobile ad hoc networks (MANE s), smartphone ad hoc networks (SPANs), andInternet-based mobile ad hoc networks (iMANETs). Wireless ad hocnetworks can beneficially provide networking that does not requireexpensive infrastructure, that uses an unlicensed frequency spectrum,that provides a quick distribution of information, and that does nothave a single point of failure.

To overcome problems caused by the frequent breakage or disconnectionand reconnection of links due to the high mobility of the nodes across-layer design can be provided that deviates from the traditionalnetwork design approach in which each layer of the stack would be madeto operate independently. A modified transmission power can help a nodeto dynamically vary its propagation range at the physical layer. This isbecause the propagation distance is generally directly proportional totransmission power. This information is passed from the physical layerto the network layer so that it can make optimal decisions in routingprotocols. This protocol can allow access of information betweenphysical layer and top layers (MAC and network layer).

Different routing protocols can be employed. In distance vector routing,each vehicle or node maintains one or more dynamically updated routingtables. Distance-vector protocols are based on calculating the directionand distance to any link in a network. “Direction” usually means thenext hop address and the exit interface. “Distance” is a measure of thecost to reach a certain node. The least cost route between any two nodesis the route with minimum distance. Each node maintains a vector (table)of minimum distance to every node. The cost of reaching a destination iscalculated using various route metrics. RIP uses the hop count of thedestination whereas IGRP takes into account other information such asnode delay and available bandwidth. In reactive routing, the node findsa route based on user and traffic demand by flooding the network withRoute Request or Discovery packets. Clustering of the vehicles can beused to limit flooding. In flooding, every incoming packet is sentthrough every outgoing link except the one it arrived on. In hybridrouting, routing is initially established with some proactivelyprospected routes and then serves the demand from additionally activatednodes through reactive flooding. In position-based routing, informationon the exact locations of the nodes is obtained for example via a GPSreceiver. Based on the exact location, the best path between source anddestination nodes can be determined.

To reduce collisions caused by nodes competing for the shared wirelessmedium, centralized scheduling or distributed contention accessprotocols can be used. Using cooperative wireless communications canimprove immunity to interference by having the destination node combineself-interference and other-node interference to improve decoding of thedesired signals.

The ad hoc wireless network can be formed in many ways. For example, theparticular vehicles in the network at any time can be based on one ormore factors, including spatial location or proximity, received signalstrength for signals received from other vehicles to be included in thenetwork, direction of travel, nature of roadway (e.g., divided or not,number of lanes, etc.), vehicle type, model or manufacturer, and otherfactors appreciated by one of skill in the art.

To avoid inter-vehicle conflicts as to which vehicle in the network isthe master vehicle with the ad hoc network, an arbitration process canbe employed. The arbitration process can be based on any technique knownin the art for other applications, including ownership of a token,earliest timestamp of receiving selected navigation or autonomousdriving information, oldest timestamp of membership in the network,nearest vehicle to the relevant feature or location for the selectednavigation or autonomous driving information, vehicle route selected,and the like.

Membership of the ad hoc wireless network and member role can changedynamically as vehicles move in and out of proximity to each other.Vehicles therefore can send notifications that they are leaving thenetwork or requests to be admitted to the network. The network can besubstantially fixed at a set of spatial map coordinates or move with adesignated master vehicle. As will be appreciated, multiple ad hocnetworks generally exist at any one time involving different sets ofvehicles with different sets of members. A vehicle is generally a memberof only one of the many ad hoc networks used by the control source andnavigation source.

To avoid duplication, the master vehicle sends notifications to thenavigation source 356A or control source 356B identifying (by electronicaddress or other unique identifier) which vehicles in the ad hocwireless network (a) acknowledged receipt of the selected navigationinformation or autonomous driving information to avoid duplicatetransmission of the information by the appropriate one of the navigationsource and control source or (b) acknowledged receipt of notificationthat certain types of collected information would be transmitted by themaster vehicle and not by the other vehicle in the network.

The autonomous driving agent can be configured to handle otherautonomous operations, regardless of automation level. Examples includeadaptive cruise control, lane keeping, parking functions, and the like.

The operations of the various executable modules will now be discussedwith reference to FIGS. 10-13.

With reference to FIG. 10, the autonomous driving agent 904, in step1000, detects a stimulus, such as any set forth above, and commencesexecution of the instructions. Exemplary stimuli include, for example,detection of a change in any of the previously sensed vehicle location,sensed object information 970, sensed occupant information 916,vehicle-related information 982, exterior environmental information 986,and/or navigation information 924 and/or in learned autonomous drivinginformation 974.

In step 1004, the autonomous driving agent 904 determines from theautomatic vehicle location system 908 the current geographical locationof the vehicle 100.

In step 1008, the autonomous driving agent 904 collects vehicle-relatedinformation 982 from the sensor connectivity manager 566.

In step 1012, the autonomous driving agent 904 collects occupant-relatedinformation 916, such as the information set forth above. This includes,for example, the identities of the vehicle occupants, the roles of eachidentified occupant (e.g., driver or passenger), a current activity ofeach occupant (e.g., operating vehicle, operating portable computingdevice, interacting with an on board vehicle user interface, and thelike), gaze detection of an occupant, and the like.

In step 1016, the autonomous driving agent 904 collects sensed exteriorenvironmental information 986 from the sensor connectivity manager 566.

In step 1020, the autonomous driving agent 908 collects sensed animateand inanimate object information 970 from the sensor connectivitymanager 566.

In step 1024, the autonomous driving agent 908 forwards all or part ofthe foregoing collected information, via communications subsystem 350and network 352, to the navigation or control source as appropriate. Asnoted, how much of the collected information is transmitted can dependon whether or not the vehicle of the autonomous driving agent is themaster or slave vehicle in the ad hoc network comprising the vehicle. Ingeneral, the types of collected information unique to the vehicle,including sensed occupant information 916, vehicle location, andvehicle-related information 982 is always transmitted by the vehicle,whether acting as a master or slave vehicle, while the types ofcollected information that are common to the vehicles in the network,including sensed object information 970 and environmental information986, is generally transmitted by the master vehicle and not the slavevehicles.

With reference to FIG. 11, the navigation source and/or control source,in step 1100, receives the collected information from the autonomousdriving agent of the selected vehicle along with a unique identifier ofthe vehicle (such as an identity of the owner, electronic address of thevehicle, serial number or vehicle identification number of the vehicle,and the like).

In step 1104, the navigation source and/or control source stores thecollected information in the control source database along with theunique vehicle identifier and/or an identifier of an occupant of thevehicle 100.

In step 1108, the control source identifies autonomous drivinginformation for publication to other autonomous vehicles and selectslimitation(s), if any, on the identified autonomous driving information.This step can be based on recognition of a novel behavior by thereporting vehicle, a previously unknown hazard or object encountered bythe reporting vehicle at all map locations or at the respective maplocation, at least a minimum frequency of usage of the behavior bymultiple vehicles, including the reporting vehicle, at the respectivemap location, and the like.

In step 1112, the control source determines whether to select otherautonomous vehicles to receive the identified autonomous drivinginformation or to embed autonomous driving information in navigationinformation provided by the navigation source. This determination can bebased for instance on one or more of the criteria referenced in step1108.

In step 1116, the control source causes the identified autonomousdriving information to be pushed to selected autonomous vehicles orincluded by the navigation source in navigation information.

With reference to FIG. 12, the autonomous driving agent 904, in step1200, detects a stimulus, such as any set forth above, and commencesexecution of the instructions. Exemplary stimuli include, for example,activation of a level of autonomous operation, increase to a higher ordecrease to a lower level of autonomous operation (e.g., from Level 0 toLevel 1, Level 1 to Level 2, Level 2 to Level 3, or Level 3 to Level 4),sensed vehicle location having a certain set of values, user input, ordetection of a change in any of the previously sensed vehicle location,sensed object information 970, sensed occupant information 916,vehicle-related information 982, exterior environmental information 986,and/or navigation information 924.

In step 1204, the behavior selector retrieves or receives learned andidentified autonomous driving information and determines which of thelearned and identified autonomous driving information is to be employedfor the current vehicle and in modeling behavior of nearby obj ects.

In step 1208, the autonomous driving agent 904 determines from theautomatic vehicle location system 908 a current location of the selectedvehicle and receives from the sensor connectivity manager 966 currentvehicle-related and occupant-related information 982 and 916 andexterior environmental and object information 986 and 970.

In step 1212, the autonomous driving agent 904 processes the determinedinformation to provide contextual information identifying nearbyobjects, relevant map information, signage, and other factors.

In step 1216, the autonomous driving agent 904, based on the contextualinformation and learned or identified autonomous driving information,predicts a behavior of the nearby objects.

In step 1220, the autonomous driving agent, based on current vehicle-and occupant-related information 982 and 916, exterior environmental andobject information 986 and 970, and other contextual information and thepredicted behavior of nearby objects and the selected one of the learnedand identified autonomous driving information, determines accelerationevents, deceleration events, acceleration rate, deceleration rate,steering angle and inter-object spacing.

In step 1224, the autonomous driving agent then issues appropriatecommands to other vehicle components, such as steering, braking, andthrottle assemblies, to execute the determined instructions.

With reference to FIG. 13, the autonomous driving agent, in step 1300,detects a stimulus, such as any set forth above, and commences executionof the instructions. Exemplary stimuli include, for example, activationof a level of autonomous operation, increase to a higher or decrease toa lower level of autonomous operation (e.g., from Level 0 to Level 1,Level 1 to Level 2, Level 2 to Level 3, or Level 3 to Level 4), sensedvehicle location having a certain set of values, user input, ordetection of a change in any of the previously sensed vehicle location,sensed object information 970, sensed occupant information 916,vehicle-related information 982, exterior environmental information 986,and/or navigation information 924.

In step 1304, the autonomous driving agent determines a current vehiclelocation.

In step 1308, the autonomous driving agent, when not currently in an adhoc network, identifies nearby vehicles.

In optional step 1312, the autonomous driving agent determines whetheran ad hoc network already includes one or more of the identified nearbyvehicles and, if so, joins the appropriate ad hoc network.

In optional step 1316, the autonomous driving agent determines that noad hoc network has yet been formed and determines which nearby vehiclesto include in a newly formed ad hoc network.

In optional step 1320, the autonomous driving agent forms the ad hocnetwork.

In step 1324, the autonomous determines which vehicle in the existing ornewly formed ad hoc network is or should be the master vehicle and whichvehicle(s) are the slave vehicles and adopts and implements theappropriate role in the ad hoc network.

With reference to FIG. 14, the logical instructions are executed by anarithmetic/logic unit (“ALU”), which performs mathematical operations,such as addition, subtraction, multiplication, and division, machineinstructions, an address bus (that sends an address to memory), a databus (that can send data to memory or receive data from memory), a readand write line to tell the memory whether to set or get the addressedlocation, a clock line that enables a clock pulse to sequence theprocessor, and a reset line that resets the program counter to zero oranother value and restarts execution. The arithmetic/logic unit can be afloating point processor that performs operations on floating pointnumbers. The autonomous driving agent 904, behavior selector, controlsource and/or navigation source further includes first, second, andthird registers that are typically configured from flip-flops, anaddress latch, a program counter (which can increment by “1” and resetto “0”), a test register to hold values from comparisons performed inthe arithmetic/logic unit (such as comparisons in any of the steps inFIGS. 10-13), plural tri-state buffers to pass a “1” or “0” ordisconnect its output (thereby allowing multiple outputs to connect to awire but only one of them to actually drive a “1” or “0” into the line),and an instruction register and decoder to control other components.Control lines, in the autonomous driving agent 904, behavior selector,control source and/or navigation source, from the instruction decodercan: command the first register to latch the value currently on the databus, command the second register to latch the value currently on thedata bus, command the third register to latch the value currently outputby the ALU, command the program counter register to latch the valuecurrently on the data bus, command the address register to latch thevalue currently on the data bus, command the instruction register tolatch the value currently on the data bus, command the program counterto increment, command the program counter to reset to zero, activate anyof the plural tri-state buffers (plural separate lines), command the ALUwhat operation to perform, command the test register to latch the ALU'stest bits, activate the read line, and activate the write line. Bitsfrom the test register and clock line as well as the bits from theinstruction register come into the instruction decoder. Hardware similaror identical to that of FIG. 14 is in each of the autonomous drivingagent 904, behavior selector, control source and/or navigation sourcefor executing the instructions of FIGS. 10-13. The ALU executesinstructions for a random or pseudo-random number generation algorithmand generates the recipient identifier using the appropriate seedvalues.

Any of the steps, functions, and operations discussed herein can beperformed continuously and automatically.

The exemplary systems and methods of this disclosure have been describedin relation to vehicle systems and electric vehicles. However, to avoidunnecessarily obscuring the present disclosure, the precedingdescription omits a number of known structures and devices. Thisomission is not to be construed as a limitation of the scope of theclaimed disclosure. Specific details are set forth to provide anunderstanding of the present disclosure. It should, however, beappreciated that the present disclosure may be practiced in a variety ofways beyond the specific detail set forth herein.

Furthermore, while the exemplary embodiments illustrated herein show thevarious components of the system collocated, certain components of thesystem can be located remotely, at distant portions of a distributednetwork, such as a LAN and/or the Internet, or within a dedicatedsystem. Thus, it should be appreciated, that the components of thesystem can be combined into one or more devices, such as a server,communication device, or collocated on a particular node of adistributed network, such as an analog and/or digital telecommunicationsnetwork, a packet-switched network, or a circuit-switched network. Itwill be appreciated from the preceding description, and for reasons ofcomputational efficiency, that the components of the system can bearranged at any location within a distributed network of componentswithout affecting the operation of the system.

Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information. Transmission media usedas links, for example, can be any suitable carrier for electricalsignals, including coaxial cables, copper wire, and fiber optics, andmay take the form of acoustic or light waves, such as those generatedduring radio-wave and infra-red data communications.

While the flowcharts have been discussed and illustrated in relation toa particular sequence of events, it should be appreciated that changes,additions, and omissions to this sequence can occur without materiallyaffecting the operation of the disclosed embodiments, configuration, andaspects.

A number of variations and modifications of the disclosure can be used.It would be possible to provide for some features of the disclosurewithout providing others.

In yet another embodiment, the systems and methods of this disclosurecan be implemented in conjunction with a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit element(s), an ASIC or other integrated circuit, a digitalsignal processor, a hard-wired electronic or logic circuit such asdiscrete element circuit, a programmable logic device or gate array suchas PLD, PLA, FPGA, PAL, special purpose computer, any comparable means,or the like. In general, any device(s) or means capable of implementingthe methodology illustrated herein can be used to implement the variousaspects of this disclosure. Exemplary hardware that can be used for thepresent disclosure includes computers, handheld devices, telephones(e.g., cellular, Internet enabled, digital, analog, hybrids, andothers), and other hardware known in the art. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis disclosure is dependent on the speed and/or efficiency requirementsof the system, the particular function, and the particular software orhardware systems or microprocessor or microcomputer systems beingutilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this disclosurecan be implemented as a program embedded on a personal computer such asan applet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Although the present disclosure describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Other similar standards and protocols not mentioned hereinare in existence and are considered to be included in the presentdisclosure. Moreover, the standards and protocols mentioned herein andother similar standards and protocols not mentioned herein areperiodically superseded by faster or more effective equivalents havingessentially the same functions. Such replacement standards and protocolshaving the same functions are considered equivalents included in thepresent disclosure.

The present disclosure, in various embodiments, configurations, andaspects, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious embodiments, subcombinations, and subsets thereof. Those ofskill in the art will understand how to make and use the systems andmethods disclosed herein after understanding the present disclosure. Thepresent disclosure, in various embodiments, configurations, and aspects,includes providing devices and processes in the absence of items notdepicted and/or described herein or in various embodiments,configurations, or aspects hereof, including in the absence of suchitems as may have been used in previous devices or processes, e.g., forimproving performance, achieving ease, and/or reducing cost ofimplementation.

The foregoing discussion of the disclosure has been presented forpurposes of illustration and description. The foregoing is not intendedto limit the disclosure to the form or forms disclosed herein. In theforegoing Detailed Description for example, various features of thedisclosure are grouped together in one or more embodiments,configurations, or aspects for the purpose of streamlining thedisclosure. The features of the embodiments, configurations, or aspectsof the disclosure may be combined in alternate embodiments,configurations, or aspects other than those discussed above. This methodof disclosure is not to be interpreted as reflecting an intention thatthe claimed disclosure requires more features than are expressly recitedin each claim. Rather, as the following claims reflect, inventiveaspects lie in less than all features of a single foregoing disclosedembodiment, configuration, or aspect. Thus, the following claims arehereby incorporated into this Detailed Description, with each claimstanding on its own as a separate preferred embodiment of thedisclosure.

Moreover, though the description of the disclosure has includeddescription of one or more embodiments, configurations, or aspects andcertain variations and modifications, other variations, combinations,and modifications are within the scope of the disclosure, e.g., as maybe within the skill and knowledge of those in the art, afterunderstanding the present disclosure. It is intended to obtain rights,which include alternative embodiments, configurations, or aspects to theextent permitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges, or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges, or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

Embodiments include a vehicle comprising:

a vehicle interior for receiving one or more occupants;

a plurality of sensors to collect sensed information associated with thevehicle interior and an exterior of the vehicle;

an automatic vehicle location system to determine a current spatiallocation of the vehicle;

a computer readable medium to store networking instructions to form anad hoc network comprising the vehicle and a different vehicle; and

a microprocessor, coupled to the plurality of sensors, automatic vehiclelocation system, and computer readable medium, that transmits one ormore of navigation information and autonomous driving information to thedifferent vehicle for execution by a microprocessor of the differentvehicle and wherein the vehicle and different vehicle independentlytransmit, to a control source or navigation source, a first type ofsensed information collected by a respective plurality of sensors andcollectively transmit, to the control source or navigation source, asecond type of sensed information collected by the plurality of sensorsof the vehicle only and not from the plurality of sensors of thedifferent vehicle.

Embodiments include a method that includes the steps:

collecting, by a plurality of sensors of first and second vehicles,sensed information associated with an interior of the correspondingvehicle and an exterior of the corresponding vehicle;

forming, by a microprocessor of the first vehicle, an ad hoc networkcomprising the first and second vehicles;

transmitting, by the microprocessor of the first vehicle, one or more ofnavigation information and autonomous driving information to the secondvehicle for execution by a microprocessor of the second vehicle;

transmitting, by microprocessors of each of the first and secondvehicles and to a control source or navigation source, a first type ofsensed information collected by a respective plurality of sensors in thefirst and second vehicles; and

transmitting, by the microprocessor of the first vehicle but not by themicroprocessor of the second vehicle, a second type of sensedinformation to the control source or navigation source, the second typeof sensed information being collected by the plurality of sensors of thefirst vehicle and not by the plurality of sensors of the second vehicle.

Embodiments include a method that includes the steps:

receiving, by a microprocessor, a first type of sensed informationtransmitted independently from first and second vehicles at differenttimes, the first type of sensed information received from the first andsecond vehicles being different and being received with first and secondidentifiers sent by the first and second vehicles, respectively, thefirst and second identifiers being associated with the first and secondvehicles;

receiving, by the microprocessor, a second type of sensed informationtransmitted from the first vehicle, wherein the first and second typesof information received from the first vehicle were collected at a sametime; and

storing, by the microprocessor, the first and second type of sensedinformation in association with the first and second identifiers torepresent sensed information received from both vehicles.

Aspects of the above vehicle or method can include one or more of: thefirst type of sensed information comprising one or more ofoccupant-related and vehicle-related information and the second type ofsensed information comprising one or more of sensed object informationand environmental information and the vehicle transmitting to thecontrol source or navigation source an identifier associated with thevehicle and different vehicle in connection with the second type ofsensed information.

Aspects of the above vehicle or method can include one or more of: whatvehicles are in the ad hoc network is based on one or more of spatiallocation or proximity of potentially networked vehicles, received signalstrength by one potentially networked vehicle of a signal transmitted byanother potentially networked vehicle, directions of travel ofpotentially networked vehicles, roadway type traveled by potentiallynetworked vehicles, types of potentially networked vehicles, models ofpotentially networked vehicles, and manufacturers of potentiallynetworked vehicles and which vehicles are members of the ad hoc networkchange in response to vehicle movement.

Aspects of the above vehicle or method can include one or more of: thecomputer readable medium comprising learned autonomous drivinginformation describing a behavioral response of the vehicle to priorsensed information when the vehicle is in an autonomous mode ofoperation and identified autonomous driving information describing abehavioral response of other vehicles to sensed information collected bythe other vehicles when in an autonomous mode of operation, themicroprocessor, when in the autonomous mode of operation and for currentsensed information by the plurality of sensors, selects between one ofthe learned and identified autonomous driving information to be executedbased on the current sensed information, the learned and identifiedautonomous driving information produce different behaviors of thevehicle when in the autonomous mode based on the current sensedinformation, the behaviors correspond to one or more of an accelerationevent, an acceleration rate, a deceleration event, a deceleration rate,a steering angle relative to a reference axis, and a spacing distancebetween an exterior surface of the vehicle and a nearby object, and thesensed information further comprises one or more of sensed objectinformation associated with objects in spatial proximity to the vehicle,sensed occupant information for the vehicle, sensed vehicle-relatedinformation, and exterior environmental information regarding anenvironment of the vehicle.

Aspects of the above vehicle or method can include the identifiedautonomous driving information having a corresponding applicationlimitation defining when the identified autonomous driving informationis to be used instead of the learned autonomous driving information, andthe corresponding application limitation is one or more of a temporallimitation, a spatial limitation, and an event duration limitation.

Aspects of the above vehicle or method can include one or more of: thecurrent sensed information comprises navigation information, thenavigation information comprises a dimensional array of features, eachfeature having an attribute of location and category, the navigationinformation comprises or references the identified autonomous drivinginformation, and the identified autonomous driving information comprisesone or more of a command to the microprocessor, request to themicroprocessor, warning of a hazard, instruction to be performed by themicroprocessor, rule to be applied by the microprocessor, and link toone or more of the command, request, warning, instruction or rule, andwherein the navigation information comprises a flag field to indicatewhether or not the navigation information comprises identifiedautonomous driving information.

Aspects of the above vehicle or method can include the microprocessorforward the identified autonomous driving information to at least oneother autonomous vehicle in spatial proximity to the autonomous vehiclefor possible execution by a microprocessor of the at least one otherautonomous vehicle and the autonomous mode of operation is at leastlevel 2 or higher.

Any one or more of the aspects/embodiments as substantially disclosedherein.

Any one or more of the aspects/embodiments as substantially disclosedherein optionally in combination with any one or more otheraspects/embodiments as substantially disclosed herein.

One or means adapted to perform any one or more of the aboveaspects/embodiments as substantially disclosed herein.

The phrases “at least one,” “one or more,” “or,” and “and/or” areopen-ended expressions that are both conjunctive and disjunctive inoperation. For example, each of the expressions “at least one of A, Band C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “oneor more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more,” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers toany process or operation, which is typically continuous orsemi-continuous, done without material human input when the process oroperation is performed. However, a process or operation can beautomatic, even though performance of the process or operation usesmaterial or immaterial human input, if the input is received beforeperformance of the process or operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processor operation is not deemed to be “material.”

Aspects of the present disclosure may take the form of an embodimentthat is entirely hardware, an embodiment that is entirely software(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Any combination of one or more computer-readable medium(s) may beutilized. The computer-readable medium may be a computer-readable signalmedium or a computer-readable storage medium.

A computer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer-readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer-readable medium may be transmitted using anyappropriate medium, including, but not limited to, wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

The terms “determine,” “calculate,” “compute,” and variations thereof,as used herein, are used interchangeably and include any type ofmethodology, process, mathematical operation or technique.

The term “electric vehicle” (EV), also referred to herein as an electricdrive vehicle, may use one or more electric motors or traction motorsfor propulsion. An electric vehicle may be powered through a collectorsystem by electricity from off-vehicle sources, or may be self-containedwith a battery or generator to convert fuel to electricity. An electricvehicle generally includes a rechargeable electricity storage system(RESS) (also called Full Electric Vehicles (FEV)). Power storage methodsmay include: chemical energy stored on the vehicle in on-board batteries(e.g., battery electric vehicle or BEV), on board kinetic energy storage(e.g., flywheels), and/or static energy (e.g., by on-board double-layercapacitors). Batteries, electric double-layer capacitors, and flywheelenergy storage may be forms of rechargeable on-board electrical storage.

The term “hybrid electric vehicle” refers to a vehicle that may combinea conventional (usually fossil fuel-powered) powertrain with some formof electric propulsion. Most hybrid electric vehicles combine aconventional internal combustion engine (ICE) propulsion system with anelectric propulsion system (hybrid vehicle drivetrain). In parallelhybrids, the ICE and the electric motor are both connected to themechanical transmission and can simultaneously transmit power to drivethe wheels, usually through a conventional transmission. In serieshybrids, only the electric motor drives the drivetrain, and a smallerICE works as a generator to power the electric motor or to recharge thebatteries. Power-split hybrids combine series and parallelcharacteristics. A full hybrid, sometimes also called a strong hybrid,is a vehicle that can run on just the engine, just the batteries, or acombination of both. A mid hybrid is a vehicle that cannot be drivensolely on its electric motor, because the electric motor does not haveenough power to propel the vehicle on its own.

The term “rechargeable electric vehicle” or “REV” refers to a vehiclewith on board rechargeable energy storage, including electric vehiclesand hybrid electric vehicles.

1-19. (canceled)
 20. An autonomous vehicle, comprising: a vehicleinterior for receiving one or more occupants; a plurality of sensors tocollect vehicle-related information, occupant-related information, andexterior environmental and object information associated with thevehicle; an automatic vehicle location system to determine a currentspatial location of the vehicle; a computer readable medium to storenetworking instructions to form an ad hoc network comprising the vehicleand a different vehicle; an arithmetic logic unit that performsmathematical operations; a data bus that, at the request of thearithmetic logic unit, sends data to or receives data from the computerreadable medium; an address bus that, at the request of the arithmeticlogic unit, sends an address to the computer readable medium; a read andwrite line that, at the request of the arithmetic logic unit, commandsthe computer readable medium whether to set or retrieve a locationcorresponding to the address; one or more registers to latch a value onthe data bus or output by the arithmetic logic unit; and one or morebuffers, wherein the arithmetic logic unit is coupled to the pluralityof sensors, automatic vehicle location system, and computer readablemedium, and determines a current spatial location of the vehicle,receives current vehicle-related information, current occupant-relatedinformation, and exterior environmental and object information,generates, from the exterior environmental and object information athree-dimensional map comprising exterior animate objects in spatialproximity to the vehicle, models from the three-dimensional mappredicted behavior of one or more of the exterior animate objects andfrom the occupant-related information predicted behavior of one or morevehicle occupants, based on the three-dimensional map and predictedbehaviors of the one or more exterior animate objects and one or morevehicle occupants, issues a command to a vehicle component to perform avehicle driving operation, and communicates to the different vehicle notto transmit to a remote control source or navigation source exteriorenvironmental and object information collected by a respective pluralityof sensors of the different vehicle in temporal proximity to the vehiclecollection of the exterior environmental and object information, whereinthe vehicle transmits, to a remote control source or navigation source,the current vehicle-related information and current occupant-relatedinformation, three-dimensional map, current spatial location of thevehicle, and predicted behavior of one or more of the exterior animateobjects and/or one or more of the vehicle occupants.
 21. The vehicle ofclaim 20, wherein the different vehicle transmits currentvehicle-related information and current occupant-related informationcollected by the respective plurality of sensors of the differentvehicle to the remote control source or navigation source but does nottransmit navigation source exterior environmental and object informationcollected by a respective plurality of sensors of the different vehicle.22. The vehicle of claim 20, wherein the microprocessor transmits thepredicted behaviors of the one or more exterior animate objects to thedifferent vehicle for execution by a microprocessor of the differentvehicle.
 23. The vehicle of claim 20, wherein the microprocessortransmits the predicted behaviors of the one or more occupants of thevehicle to the different vehicle for execution by a microprocessor ofthe different vehicle.
 24. The vehicle of claim 20, wherein theplurality of sensors comprise a lidar sensor, radar sensor, ultrasonicsensor, camera, and infrared sensor, wherein the command is one or moreof an acceleration rate of the vehicle, a deceleration rate of thevehicle, a steering angle of the vehicle, and an inter-object spacing ofthe vehicle relative to an exteriorly located object, and wherein thevehicle transmits to the control source or navigation source anidentifier associated with the vehicle and different vehicle inconnection with the in connection with the three-dimensional map. 25.The vehicle of claim 20, wherein what vehicles are in the ad hoc networkis based on one or more of spatial location or proximity of potentiallynetworked vehicles, received signal strength by one potentiallynetworked vehicle of a signal transmitted by another potentiallynetworked vehicle, directions of travel of potentially networkedvehicles, roadway type traveled by potentially networked vehicles, typesof potentially networked vehicles, models of potentially networkedvehicles, and manufacturers of potentially networked vehicles andwherein which vehicles are members of the ad hoc network change inresponse to vehicle movement.
 26. The vehicle of claim 20, wherein thepredicted behavior is for a selected exterior animate object in thethree-dimensional map, wherein the vehicle receives a different secondpredicted behavior of the selected exterior animate object generated byanother vehicle, and based on the three-dimensional map and the firstand second predicted behaviors of the selected exterior animate object,and issues a command to a vehicle component to perform a vehicle drivingoperation, wherein the first and second predicted behaviors, eachexecuted alone by the arithmetic logic unit, produce different commands,wherein the command corresponds to one or more of an acceleration event,an acceleration rate, a deceleration event, a deceleration rate, asteering angle relative to a reference axis, and a spacing distancebetween an exterior surface of the vehicle and a nearby object.
 27. Thevehicle of claim 26, wherein the second predicted behavior has acorresponding application limitation defining when the second predictedbehavior is to be used instead of the first predicted behavior, andwherein the corresponding application limitation is one or more of atemporal limitation, a spatial limitation, and an event durationlimitation
 28. The vehicle of claim 27, wherein the second predictedbehavior is received as part of navigation information comprising adimensional array of features, each feature having an attribute oflocation and category and wherein the navigation information comprisesor references the second predicted behavior, wherein the navigationinformation further comprises one or more of a command to the vehiclearithmetic logic unit, request to the vehicle arithmetic logic unit,warning of a hazard, instruction to be performed by the vehiclearithmetic logic unit, rule to be applied by the vehicle arithmeticlogic unit, and link to one or more of the command, request, warning,instruction or rule, wherein the navigation information comprises a flagfield to indicate whether or not the navigation information comprises apredicted behavior to be considered by the vehicle, and wherein themicroprocessor forward the identified autonomous driving information toat least one other autonomous vehicle in spatial proximity to theautonomous vehicle for possible execution by a microprocessor of the atleast one other autonomous vehicle, and wherein an autonomous mode ofoperation of the vehicle is at least level 2 or higher.
 29. A method forautonomous operation of a vehicle, comprising: providing a vehiclecomprising a vehicle interior for receiving one or more occupants, aplurality of sensors to collect vehicle-related information,occupant-related information, and exterior environmental and objectinformation associated with the vehicle, an automatic vehicle locationsystem to determine a current spatial location of the vehicle, acomputer readable medium to store networking instructions to form an adhoc network comprising the vehicle and a different vehicle, anarithmetic logic unit that performs mathematical operations, a data busthat, at the request of the arithmetic logic unit, sends data to orreceives data from the computer readable medium, an address bus that, atthe request of the arithmetic logic unit, sends an address to thecomputer readable medium, a read and write line that, at the request ofthe arithmetic logic unit, commands the computer readable medium whetherto set or retrieve a location corresponding to the address, one or moreregisters to latch a value on the data bus or output by the arithmeticlogic unit, and one or more buffers; determining, by the arithmeticlogic unit, a current spatial location of the vehicle; receiving, by thearithmetic logic unit and from the plurality of sensors, currentvehicle-related information, current occupant-related information, andexterior environmental and object information; generating, by thearithmetic logic unit, from the exterior environmental and objectinformation a three-dimensional map comprising exterior animate objectsin spatial proximity to the vehicle; determining, by the arithmeticlogic unit executing one or more behavioral models and based on thethree-dimensional map, a predicted behavior of one or more of theexterior animate objects and from the occupant-related information apredicted behavior of one or more vehicle occupants; based on thethree-dimensional map and predicted behaviors of the one or moreexterior animate objects and one or more vehicle occupants, issuing, byarithmetic logic unit, a command to a vehicle component to perform avehicle driving operation; causing, by the arithmetic logic unit, thedifferent vehicle not to transmit to a remote control source ornavigation source exterior environmental and object informationcollected by a respective plurality of sensors of the different vehiclein temporal proximity to the vehicle collection of the exteriorenvironmental and object information; and causing, by the arithmeticlogic unit, transmission, to a remote control source or navigationsource, of the current vehicle-related information and currentoccupant-related information, three-dimensional map, current spatiallocation of the vehicle, and predicted behavior of one or more of theexterior animate objects and/or one or more of the vehicle occupants.30. The method of claim 29, wherein the different vehicle transmitscurrent vehicle-related information and current occupant-relatedinformation collected by the respective plurality of sensors of thedifferent vehicle to the remote control source or navigation source butdoes not transmit navigation source exterior environmental and objectinformation collected by a respective plurality of sensors of thedifferent vehicle.
 31. The method of claim 29, wherein the arithmeticlogic unit causes transmission of the predicted behaviors of the one ormore exterior animate objects to the different vehicle for execution bya microprocessor of the different vehicle.
 32. The method of claim 29,wherein the arithmetic logic unit causes transmission of the predictedbehaviors of the one or more occupants of the vehicle to the differentvehicle for execution by a microprocessor of the different vehicle. 33.The method of claim 29, wherein the plurality of sensors comprise alidar sensor, radar sensor, ultrasonic sensor, camera, and infraredsensor, wherein the command is one or more of an acceleration rate ofthe vehicle, a deceleration rate of the vehicle, a steering angle of thevehicle, and an inter-object spacing of the vehicle relative to anexteriorly located object, and wherein the vehicle transmits to thecontrol source or navigation source an identifier associated with thevehicle and different vehicle in connection with the three-dimensionalmap.
 34. The method of claim 29, wherein what vehicles are in the ad hocnetwork is based on one or more of spatial location or proximity ofpotentially networked vehicles, received signal strength by onepotentially networked vehicle of a signal transmitted by anotherpotentially networked vehicle, directions of travel of potentiallynetworked vehicles, roadway type traveled by potentially networkedvehicles, types of potentially networked vehicles, models of potentiallynetworked vehicles, and manufacturers of potentially networked vehiclesand wherein which vehicles are members of the ad hoc network change inresponse to vehicle movement.
 35. The method of claim 29, wherein thepredicted behavior is for a selected exterior animate object in thethree-dimensional map, wherein the vehicle receives a different secondpredicted behavior of the selected exterior animate object generated byanother vehicle, and based on the three-dimensional map and the firstand second predicted behaviors of the selected exterior animate object,and issues a command to a vehicle component to perform a vehicle drivingoperation, wherein the first and second predicted behaviors, eachexecuted alone by the arithmetic logic unit, produce different commands,wherein the command corresponds to one or more of an acceleration event,an acceleration rate, a deceleration event, a deceleration rate, asteering angle relative to a reference axis, and a spacing distancebetween an exterior surface of the vehicle and a nearby object.
 36. Themethod of claim 35, wherein the second predicted behavior has acorresponding application limitation defining when the second predictedbehavior is to be used instead of the first predicted behavior, andwherein the corresponding application limitation is one or more of atemporal limitation, a spatial limitation, and an event duration
 37. Themethod of claim 36, wherein the second predicted behavior is received aspart of navigation information comprising a dimensional array offeatures, each feature having an attribute of location and category andwherein the navigation information comprises or references the secondpredicted behavior, wherein the navigation information further comprisesone or more of a command to the vehicle arithmetic logic unit, requestto the vehicle arithmetic logic unit, warning of a hazard, instructionto be performed by the vehicle arithmetic logic unit, rule to be appliedby the vehicle arithmetic logic unit, and link to one or more of thecommand, request, warning, instruction or rule, wherein the navigationinformation comprises a flag field to indicate whether or not thenavigation information comprises a predicted behavior to be consideredby the vehicle, and wherein the microprocessor forward the identifiedautonomous driving information to at least one other autonomous vehiclein spatial proximity to the autonomous vehicle for possible execution bya microprocessor of the at least one other autonomous vehicle, andwherein an autonomous mode of operation of the vehicle is at least level2 or higher.
 38. A method for autonomous operation of a vehicle,comprising: providing a vehicle comprising a vehicle interior forreceiving one or more occupants, a plurality of sensors to collectvehicle-related information, occupant-related information, and exteriorenvironmental and object information associated with the vehicle;, anautomatic vehicle location system to determine a current spatiallocation of the vehicle, a computer readable medium to store networkinginstructions to form an ad hoc network comprising the vehicle and adifferent vehicle, an arithmetic logic unit that performs mathematicaloperations, a data bus that, at the request of the arithmetic logicunit, sends data to or receives data from the computer readable medium,an address bus that, at the request of the arithmetic logic unit, sendsan address to the computer readable medium, a read and write line that,at the request of the arithmetic logic unit, commands the computerreadable medium whether to set or retrieve a location corresponding tothe address, and one or more registers that enable the arithmetic logicunit to perform mathematical operations; determining, by the arithmeticlogic unit, a current spatial location of the vehicle; receiving, by thearithmetic logic unit, current vehicle-related information, currentoccupant-related information, and exterior environmental and objectinformation; generating, by the arithmetic logic unit, from the exteriorenvironmental and object information a three-dimensional map comprisingexterior animate objects in spatial proximity to the vehicle;determining, by the arithmetic logic unit executing one or morebehavioral models and based on the three-dimensional map, a predictedbehavior of one or more of the exterior animate objects and from theoccupant-related information a predicted behavior of one or more vehicleoccupants; based on the three-dimensional map and predicted behaviors ofthe one or more exterior animate objects and one or more vehicleoccupants, issuing, by arithmetic logic unit, a command to a vehiclecomponent to perform a vehicle driving operation; causing, by thearithmetic logic unit, a communication to be transmitted to thedifferent vehicle commanding the different vehicle not to transmit to aremote control source or navigation source exterior environmental andobject information collected by a respective plurality of sensors of thedifferent vehicle in temporal proximity to the vehicle collection of theexterior environmental and object information; causing, by thearithmetic logic unit, transmission, to a remote control source ornavigation source, of the current vehicle-related information andcurrent occupant-related information, three-dimensional map, currentspatial location of the vehicle, and predicted behavior of one or moreof the exterior animate objects and/or one or more of the vehicleoccupants; and causing, by the arithmetic logic unit, transmission ofthe predicted behaviors of the one or more exterior animate objects andone or more occupants of the vehicle to the different vehicle forexecution by a microprocessor of the different vehicle.
 39. The methodof claim 38, wherein the different vehicle transmits currentvehicle-related information and current occupant-related informationcollected by the respective plurality of sensors of the differentvehicle to the remote control source or navigation source but does nottransmit navigation source exterior environmental and object informationcollected by a respective plurality of sensors of the different vehicle.40. The method of claim 38, wherein the vehicle transmits to the controlsource or navigation source an identifier associated with the vehicleand different vehicle in connection with the three-dimensional map.